Multi-region request-driven code execution system

Description

BACKGROUND

Computing devices can utilize communication networks to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or to provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as a “data center,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public.

To facilitate increased utilization of data center resources, virtualization technologies allow a single physical computing device to host one or more instances of virtual machines that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical computing device can create, maintain, delete, or otherwise manage virtual machines in a dynamic manner. In turn, users can request computer resources from a data center, including single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources.

In some scenarios, virtual machine instances may be configured according to a number of virtual machine instance types to provide specific functionality. For example, various computing devices may be associated with different combinations of operating systems or operating system configurations, virtualized hardware resources and software applications to enable a computing device to provide different desired functionalities, or to provide similar functionalities more efficiently. These virtual machine instance type configurations are often contained within a device image, which includes static data containing the software (e.g., the OS and applications together with their configuration and data files, etc.) that the virtual machine will run once started. The device image is typically stored on the disk used to create or initialize the instance. Thus, a computing device may process the device image in order to implement the desired software configuration.

BRIEF DESCRIPTION OF DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate example embodiments described herein and are not intended to limit the scope of the disclosure.

FIG. 1 is a block diagram of an illustrative operation environment in which a plurality of POPs may implement an on-demand code execution environment and a plurality of regional data centers may implement an on-demand code execution environment.

FIG. 2A illustrates an example block diagram of the auxiliary services 122 of FIG. 1, according to one embodiment.

FIG. 2B illustrates an example block diagram of the on-demand code execution environments and of FIG. 1, according to one embodiment.

FIG. 3A is a block diagram of the operating environment of FIG. 1 illustrating the operations performed by the components of the operating environment to execute a task, according to one embodiment.

FIG. 3B is another block diagram of the operating environment of FIG. 1 illustrating the operations performed by the components of the operating environment to execute a task, according to one embodiment.

FIG. 4 is a block diagram of the operating environment of FIG. 1 illustrating the operations performed by the components of the operating environment to replicate user-defined code to one or more geographic regions, according to one embodiment.

FIG. 5 is an example table stored in the replication data store of FIG. 1, according to one embodiment.

FIG. 6 is a flow diagram depicting a task execution routine illustratively implemented by a POP, according to one embodiment.

FIG. 7 is another flow diagram depicting a task execution routine illustratively implemented by a POP, according to one embodiment.

DETAILED DESCRIPTION

Generally described, aspects of the present disclosure relate to executing user-defined code within a low latency, on-demand code execution environment, as well as managing the computing devices within the code execution environment on which the code is executed. The on-demand code execution environment may operate as part of a system of rapidly provisioned and released computing resources, often referred to as a “cloud computing environment.” Specifically, the code execution environment may include one or more computing devices, virtual or non-virtual, that are “pre-warmed” (e.g., booted into an operating system and executing a complete or substantially complete runtime environment) and configured to enable execution of user-defined code, such that the code may be executed rapidly without initializing the virtual machine instance. Each set of code on the on-demand code execution environment may define a “task,” and implement specific functionality corresponding to that task when executed on the on-demand code execution environment. Individual implementations of the task on the on-demand code execution environment may be referred to as an “execution” of the task. By defining tasks on the on-demand code execution environment and selectively executing those tasks, users may implement complex functionalities at high speed and low latency, without being required to deploy, configure, or manage the computing devices on which the tasks are executed. The on-demand code execution environment, in turn, may execute tasks of multiple users simultaneously, thus allowing efficient use of computing resources of those devices. To ensure the security and privacy of user information, the on-demand code execution environment may generally ensure that tasks of each user are executed on distinct computing devices (which may be virtual computing devices), thus reducing the chances that a task executing on behalf of a first user could interfere with or gain information regarding execution of a task on behalf of a second user.

In some instances, an on-demand code execution environment may operate as a distributed system in which multiple points of presence (POPs) implement instances of the on-demand code execution environment. As used herein, a POP is intended to refer to any collection of related computing devices utilized to implement functionality on behalf of one or many providers. POPs are generally associated with a specific geographic location in which the computing devices implementing the POP are located, or with a region serviced by the POP. For example, a data center or a collection of computing devices within a data center may form a POP. An on-demand code execution environment may utilize multiple POPs that are geographically diverse, to enable users in a variety of geographic locations to quickly transmit and receive information from the on-demand code execution environment. In some instances, the POPs may also implement other services, such as content delivery network (CDN) services, data storage services, data processing services, etc. For the purposes of the present disclosure, these other services will generally be referred to herein as “auxiliary services.” Implementation of auxiliary services and instances of the on-demand code execution environment on the same POP may be beneficial, for example, to enable tasks executed on the on-demand code execution environment to quickly interact with the auxiliary services.

However, implementation of auxiliary services on a POP may also limit the amount of computing resources available to implement an instance of the on-demand code execution environment. For example, a POP implementing an edge server of a CDN service may have relatively little available computing resources (e.g., in the form of disk space, processor time, central processing power, graphical processing power, memory, network bandwidth, internal bus utilization, etc.) with which to execute tasks. These computing resources may be even further depleted by attempting to execute those tasks within a distinct computing device, such as a virtual computing device, that does not implement functionalities of the CDN service. Moreover, the available physical space to house the edge server may be limited, thereby limiting the amount of computing resources that can be added to the POP to account for the relatively little available computing resources. In addition, dedicating a virtual or non-virtual computing device to a single user can negatively impact the performance of the on-demand code execution environment by reducing the flexibility of the environment in allocating computing resources for the execution of tasks requested by other users. Thus, if a POP receives a sufficient number of requests to execute tasks, the POP may run out of computing resource capacity to execute the tasks. Once the POP no longer has computing resource capacity to handle the execution of a task, the POP may return an exception to the requesting entity indicating that no more computing resource capacity is available to execute the task.

Accordingly, aspects of the present disclosure enable the on-demand code execution environment to be present in POPs and in regions serviced by the POPs such that tasks can be executed regardless of whether there is a sufficient amount of computing resources available at the POP to handle task requests. Thus, if a POP determines that the computing resources necessary to execute a received task are not available (e.g., the POP lacks computing resource capacity) or that the POP should not execute the received task for another reason (e.g., the task is not commonly received and the computing resources needed to execute the task are therefore best allocated for other requests), the POP can forward the task to a region that the POP services for execution by an on-demand code execution environment present in the region. For example, if the POP implements an edge server of a CDN service, the edge server may receive from a user device a request to execute a task. The edge server may then determine whether to instruct an on-demand code execution environment local to the POP to execute the task. Some factors that the edge server may consider in making the determination include how busy the POP is (e.g., the amount of unused computing resources currently available), the popularity of the requested task (e.g., how often the POP receives requests to perform the task relative to other requested tasks), a time it may take to execute the task locally as opposed to within a region, the historical volume of requests received from the user device, the time of day that the request is received, the latency-sensitivity of the task (e.g., based on an analysis of the user-defined code), properties of the task (e.g., whether execution of the user-defined code causes the retrieval of content and, if so, whether such content is available in a local POP cache or at an origin server), and/or the like.

If the edge server determines to instruct the local on-demand code execution environment to execute the task, then the edge server forwards the requested task to the local on-demand code execution environment for execution. As described in greater detail below, the local on-demand code execution environment may instruct an existing virtual machine instance to execute the requested task or may provision a new virtual machine instance and instruct the new virtual machine instance to execute the requested task. Once the execution is complete, the local on-demand code execution environment may forward the execution results to the edge server for transmission back to the user device.

If, on the other hand, the edge server determines not to instruct the local on-demand code execution environment to execute the task, then the edge server forwards the requested task to a server in a region. The edge server may forward the task to a server in the region that is closest geographically to the POP. Alternatively, the edge server may identify or select one region from a set of regions to receive the task. Some factors that the edge server may consider in making the determination include whether a region has an updated version of the user-defined code used to execute the task, whether a region has previously received a request to execute the task and/or if the region currently has a virtual machine instance provisioned to execute such tasks, and/or the like. Once the region is selected, then the edge server forwards the task to a server in the selected region.

Upon receiving the task from the edge server, the server in the region can instruct the on-demand code execution environment local to the region to execute the task. Similar to as described above, the on-demand code execution environment in the region may instruct an existing virtual machine instance to execute the requested task or may provision a new virtual machine instance and instruct the new virtual machine instance to execute the requested task. Once the execution is complete, the on-demand code execution environment in the region can forward the execution results to the region server. The region server can then transmit the execution results to the edge server in the POP for distribution to the user device.

Generally, user-defined code is specific to a region and stored therein. Thus, to enable multiple regions to execute tasks corresponding to user-defined code associated with a particular region, each region may include a replication system configured to replicate user-defined code associated with the respective region to other regions. The replication system may track which regions to replicate the user-defined code to and the status of such replications. The replication system may periodically forward this information to the POP to aid the edge server in determining which region to forward a task request.

As used herein, the term “virtual machine instance” is intended to refer to an execution of software or other executable code that emulates hardware to provide an environment or platform on which software may execute (an “execution environment”). Virtual machine instances are generally executed by hardware devices, which may differ from the physical hardware emulated by the virtual machine instance. For example, a virtual machine may emulate a first type of processor and memory while being executed on a second type of processor and memory. Thus, virtual machines can be utilized to execute software intended for a first execution environment (e.g., a first operating system) on a physical device that is executing a second execution environment (e.g., a second operating system). In some instances, hardware emulated by a virtual machine instance may be the same or similar to hardware of an underlying device. For example, a device with a first type of processor may implement a plurality of virtual machine instances, each emulating an instance of that first type of processor. Thus, virtual machine instances can be used to divide a device into a number of logical sub-devices (each referred to as a “virtual machine instance”). While virtual machine instances can generally provide a level of abstraction away from the hardware of an underlying physical device, this abstraction is not required. For example, assume a device implements a plurality of virtual machine instances, each of which emulates hardware identical to that provided by the device. Under such a scenario, each virtual machine instance may allow a software application to execute code on the underlying hardware without translation, while maintaining a logical separation between software applications running on other virtual machine instances. This process, which is generally referred to as “native execution,” may be utilized to increase the speed or performance of virtual machine instances. Other techniques that allow direct utilization of underlying hardware, such as hardware pass-through techniques, may be used, as well

While a virtual machine executing an operating system is described herein as one example of an execution environment, other execution environments are also possible. For example, tasks or other processes may be executed within a software “container,” which provides a runtime environment without itself providing virtualization of hardware. Containers may be implemented within virtual machines to provide additional security, or may be run outside of a virtual machine instance.

To execute tasks, the on-demand code execution environment described herein may maintain a pool of pre-initialized virtual machine instances that are ready for use as soon as a user request is received. Due to the pre-initialized nature of these virtual machines, delay (sometimes referred to as latency) associated with executing the user-defined code (e.g., instance and language runtime startup time) can be significantly reduced, often to sub-100 millisecond levels. Illustratively, the on-demand code execution environment may maintain a pool of virtual machine instances on one or more physical computing devices, where each virtual machine instance has one or more software components (e.g., operating systems, language runtimes, libraries, etc.) loaded thereon. When the on-demand code execution environment receives a request to execute the program code of a user, which specifies one or more computing constraints for executing the program code of the user, the on-demand code execution environment may select a virtual machine instance for executing the program code of the user based on the one or more computing constraints specified by the request and cause the program code of the user to be executed on the selected virtual machine instance. The program codes can be executed in isolated containers that are created on the virtual machine instances. Since the virtual machine instances in the pool have already been booted and loaded with particular operating systems and language runtimes by the time the requests are received, the delay associated with finding compute capacity that can handle the requests (e.g., by executing the user code in one or more containers created on the virtual machine instances) is significantly reduced.

The on-demand code execution environment may include a virtual machine instance manager configured to receive user code (threads, programs, etc., composed in any of a variety of programming languages) and execute the code in a highly scalable, low latency manner, without requiring user configuration of a virtual machine instance. Specifically, the virtual machine instance manager can, prior to receiving the user code and prior to receiving any information from a user regarding any particular virtual machine instance configuration, create and configure virtual machine instances according to a predetermined set of configurations, each corresponding to any one or more of a variety of run-time environments. Thereafter, the virtual machine instance manager receives user-initiated requests to execute code, and identify a pre-configured virtual machine instance to execute the code based on configuration information associated with the request. The virtual machine instance manager can further allocate the identified virtual machine instance to execute the user's code at least partly by creating and configuring containers inside the allocated virtual machine instance. Various embodiments for implementing a virtual machine instance manager and executing user code on virtual machine instances is described in more detail in U.S. patent application Ser. No. 14/502,648, entitled “PROGRAMMATIC EVENT DETECTION AND MESSAGE GENERATION FOR REQUESTS TO EXECUTE PROGRAM CODE” and filed Sep. 30, 2014 (hereinafter referred to as “the '648 application”), the entirety of which is hereby incorporated by reference herein. Additional details regarding the on-demand code execution environment are provided in U.S. patent application Ser. No. 14/971,859, entitled “EXECUTION LOCATIONS FOR REQUEST-DRIVEN CODE” and filed Dec. 16, 2015 (hereinafter referred to as “the '859 application”), the entirety of which is hereby incorporated by reference herein.

The foregoing aspects and many of the attendant advantages of this disclosure will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings.

Example Multi-Region On-Demand Code Execution Environment

FIG. 1 is a block diagram of an illustrative operating environment 100 in which a plurality of POPs 120 may implement an on-demand code execution environment 124 and a plurality of regional data centers 130 may implement an on-demand code execution environment 132. The POPs 120 may further implement auxiliary services 122 and a user and task history data store 126. Various user devices 102 may communicate with the POPs 120 via a network 110 to request the execution of tasks. Tasks may be written, by way of non-limiting example, in JavaScript (e.g., node.js), Java, Python, and/or Ruby (and/or another programming language). Requests to execute a task may generally be referred to as “calls” to that task. Such calls may include the user-defined code (or the location thereof) to be executed and one or more arguments to be used for executing the user-defined code. For example, a call may provide the user-defined code of a task along with the request to execute the task. In another example, a call may identify a previously uploaded task by its name or an identifier. In yet another example, code corresponding to a task may be included in a call for the task, as well as being uploaded in a separate location (e.g., storage of auxiliary services 122 or a storage system internal to the on-demand code execution environments 124 and/or 132) prior to the request being received by the on-demand code execution environments 124 and/or 132. The on-demand code execution environments 124 and/or 132 may vary its execution strategy for a task based on where the code of the task is available at the time a call for the task is processed.

While the user devices 102 and POPs 120 are shown as grouped within FIG. 1, the user devices 102 and POPs 120 may be geographically distant, and independently owned or operated. For example, the user devices 102 could represent a multitude of users in various global, continental, or regional locations accessing the POPs 120. Further, the POPs 120 may be globally, continentally, or regionally disparate, in order to provide a wide geographical presence for the on-demand code execution environment 124 and/or the auxiliary services 122. Accordingly, the groupings of user devices 102 and POPs 120 within FIG. 1 is intended to represent a logical, rather than physical, grouping. The POPs 120 of FIG. 1 are illustratively shown as implementing both auxiliary services 122 and instances of the on-demand code execution environment 124. However, the operating environment 100 may additionally or alternatively include POPs that execute only auxiliary services 122 or only an instance of the on-demand code execution environment 124. Components of the auxiliary services 122 are described in greater detail below with respect to FIG. 2A and components of the on-demand code execution environments 124 and 132 are described in greater detail below with respect to FIG. 2B.

As illustrated in FIG. 1, each regional data center 130 may include a region server 131, an on-demand code execution environment 132, a replication system 133, a cache data store 134, a replication data store 135, and a code usage data store 136. The components of the regional data center 130 may be capable of storing user-defined code, replicating user-defined code to other regional data centers 130, and executing tasks. The regional data centers 130 may each be globally, continentally, or regionally disparate. For example, each regional data center 130 may located within a geographical region serviced by a POP 120. While FIG. 1 illustrates each POP 120 communicating with a single regional data center 130, this is not meant to be limiting. A single POP 120 may communicate with one or more regional data centers 130 (e.g., regional data centers 130 that are within a threshold distance of the location of the POP 120).

The operating environment 100 further includes one or more origin servers 104. The origin servers 104 may include any computing device owned or operated by an entity that has provided one or more sets of content (“distributions”) to a CDN for subsequent transmission to user devices 102. For example, origin servers 104 may include servers hosting web sites, streaming audio, video, or multimedia services, data analytics services, or other network-accessible services. The origin servers 104 may include primary versions of content within various distributions. If the POPs 120 function as CDNs, the primary versions of content may be retrieved by the various POPs 120 for subsequent transmission to the user devices 102. In an embodiment, the POPs 120 includes a cache that stores frequently-requested content (e.g., service data store 208) and the regional data centers 130 include caches that store frequently-requested content (e.g., the cache data store 134). If requested content is not present in the POP 120 cache, the POP 120 may first request the content from the regional data center 130. If the requested content is also not present in the cache data store 134, then the POP 120 may retrieve the content from an origin server 104. In addition, origin servers 104 may be included in a regional data center 130, in addition to or as an alternative to cache data store 134.

Users, by way of user devices 102, may interact with the on-demand code execution environments 124 and/or 132 to provide executable code, and establish rules or logic defining when and how such code should be executed on the on-demand code execution environments 124 and/or 132. For example, a user may wish to run a piece of code in connection with a web or mobile application that the user has developed. One way of running the code would be to acquire virtual machine instances from service providers who provide infrastructure as a service, configure the virtual machine instances to suit the user's needs, and use the configured virtual machine instances to run the code. In order to avoid the complexity of this process, the user may alternatively provide the code to the on-demand code execution environments 124 and/or 132, and request that the on-demand code execution environments 124 and/or 132 execute the code using one or more pre-established virtual machine instances. The on-demand code execution environments 124 and 132 can handle the acquisition and configuration of compute capacity (e.g., containers, instances, etc., which are described in greater detail below) based on the code execution request, and execute the code using the compute capacity. The on-demand code execution environments 124 and/or 132 may automatically scale up and down based on the volume, thereby relieving the user from the burden of having to worry about over-utilization (e.g., acquiring too little computing resources and suffering performance issues) or under-utilization (e.g., acquiring more computing resources than necessary to run the codes, and thus overpaying).

In an embodiment, a user device 102 may transmit a request to execute a task to POP 120A or 120B via the network 110. The request may be received by a server in the auxiliary services 122 (e.g., server 207). The server may then determine whether to execute the task locally or to forward the task to a region server 131 in one of the regional data centers 130. For example, the server may use information stored in the user and task history data store 126 as well as information identifying how busy the POP 120 is (e.g., the amount of unused computing resources currently available to the POP 120), a time it may take to execute the task locally at the POP 120 (e.g., which is based on properties of the user-defined code used to execute the task and the available computing resources, such as whether a virtual machine instance is already provisioned to execute the task), a time it may take to execute the task within a regional data center 130 (e.g., which is based on properties of the user-defined code used to execute the task and whether a virtual machine instance is already provisioned in the on-demand code execution environment 132 to execute the task), the time of day that the request is received, and/or the like to determine whether to execute the task locally or to forward the task to a region server 131.

The information stored in the user and task history data store 126 may include the popularity of the requested task (e.g., how often the POP 120 receives requests to perform the task relative to other requested tasks), the historical volume of requests received from the user device 102 that requested the task execution, the latency-sensitivity of the task, properties of the task (e.g., what type of content is retrieved as a result of execution of the task), and/or the like. The latency-sensitivity of the task and/or the properties of the task may be determined based on a previous or current analysis of the user-defined code. As an example, the user-defined code may be stored within a regional data center 130 that is associated with the same region as the user-defined code. The server in the auxiliary service 122 may retrieve the user-defined code from the appropriate regional data center 130 (e.g., at a previous time so that the POP 120 could execute a previously-received task or at a current time) and analyze the user-defined code to estimate whether a use case associated with the task is sensitive to delays in task execution and/or to identify other properties of the task. As an illustrative example, based on an analysis of the user-defined code, the server may determine that execution causes a script to be run (e.g., Javascript). Other components or code modules may be dependent on the completion of the script (e.g., other components to be loaded in order to display a network or content page) and therefore the task may be latency sensitive. On the other hand, the analysis may result in a determination that the task relates to decompressing bits, which is less latency sensitive. As another illustrative example, based on an analysis of the user-defined code, the server may determine that execution of the task results in the retrieval of content stored in an origin server 104.

The server may consider some or all of the above-described factors independently or in combination to determine whether to execute the task locally or remotely in a regional data center 130. For example, if the server determines that the amount of unused computing resources currently available to the POP 120 is not sufficient to execute the task, then the server determines to forward the task to a region server 131. Otherwise, if the server determines that the amount of unused computing resources currently available to the POP 120 is sufficient to execute the task and no other factors indicate that the task should be executed remotely, then the server may instruct the on-demand code execution environment 124 to execute the task.

As another example, if the server determines that the time it would take to execute the task locally at the POP 120 is faster than the time it would take to execute the task remotely in a regional data center 130, then the server may instruct the on-demand code execution environment 124 to execute the task. Otherwise, if the server determines that the time it would take to execute the task locally at the POP 120 is slower than the time it would take to execute the task remotely in a regional data center 130, then the server may forward the task to the regional data center 130.

As another example, if the task is not commonly received (e.g., the task has not been received before, the number of times the task has been received is below a threshold value, the number of times the task has been received as a percentage of a number of times any task has been received is below a threshold value, etc.) and/or the user device 102 typically sends a low volume of requests (e.g., the user device 102 sends a number of requests below a threshold value, the percentage of requests sent by the user device 102 as a percentage of all requests received is below a threshold value, a time required by the user device 102 to execute all desired tasks is below a threshold value, etc.), then the server may forward the task to the regional data center 130. Provisioning a virtual machine instance to service a user device 102 and/or request and tearing down the virtual machine instance when tasks are completed can be time and resource intensive, and therefore the server may determine to offload the task to a regional data center 130 if the task is uncommon or the user device 102 sends a low volume of requests (and thus does not require the virtual machine instance for a long period of time). Otherwise, if the task is commonly received (e.g., the number of times the task has been received is above a threshold value, the number of times the task has been received as a percentage of a number of times any task has been received is above a threshold value, etc.) and/or the user device 102 typically sends a high volume of requests (e.g., the user device 102 sends a number of requests above a threshold value, the percentage of requests sent by the user device 102 as a percentage of all requests received is above a threshold value, a time required by the user device 102 to execute all desired tasks is above a threshold value, etc.), then the server may instruct the on-demand code execution environment 124 to execute the task.

As another example, if the task request is received during a time of day in which the POP 120 is busy (e.g., a threshold percentage of the POP 120 computing resources are occupied) and the task is not commonly received and/or the user device 102 typically sends a low volume of requests, then the server may forward the task to the regional data center 130. Otherwise, if the task request is received during a time of day in which the POP 120 is busy and the task is commonly received and/or the user device 102 typically sends a high volume of requests and/or if the task request is receive during a time of day in which the POP 120 is not busy, then the server may instruct the on-demand code execution environment 124 to execute the task.

As another example, if the server determines that the task is latency sensitive, then the server may instruct the on-demand code execution environment 124 to execute the task. Requesting the on-demand code execution environment 124 to execute the task may be faster than the regional data center 130 because of the fewer number of transmissions required to complete execution of the task (e.g., the server may not communicate directly with the on-demand code execution environment 132, so there may be additional transmissions required to forward the task to the on-demand code execution environment 132). Otherwise, if the server determines that the task is not latency sensitive and no other factors indicate that the task should be executed locally, then the server may forward the task to the regional data center 130.

As another example, if the server determines that the user-defined code properties indicate that execution of the task results in the retrieval of content stored in an origin server 104, then the server may forward the task to the regional data center 130. As described above, the POP 120 may send a request for content to the regional data center 130 first before sending a request to the origin server 104 if the content is not available in the POP 120 cache. Thus, given the limited computing resources available at the POP 120, the server may forward the task to be executed remotely because a request may be sent to the regional data center 130 anyway. Otherwise, the server may instruct the on-demand code execution environment 124 to execute the task if no other factors indicate that the task should be executed remotely.

In an embodiment, once the server determines to forward the task to a regional data center 130, the server forwards the task to the region server 131 in the regional data center 130 that is geographically closest to the POP 120. For example, the regional data center 130A may be the closest regional data center to the POP 120A. Thus, the server in the auxiliary services 122A may forward the task to the region server 131A.

In other embodiments, once the server determines to forward the task to a regional data center 130, the server then determines which regional data center 130 to send the request to. For example, it may be more efficient to forward the task to a first regional data center 130 than a second regional data center 130 even if the first regional data center 130 is farther geographically from the POP 120 than the second regional data center 130. Such situations may occur when the first regional data center 130 has received requests to execute the task before and therefore has provisioned a virtual machine instance to execute the task, whereas the second regional data center 130 has not received requests to execute the task or has not received many requests to execute the task and the time required to provision a virtual machine instance to execute the task would be longer than the time required to send the task to the first regional data center 130 for execution. In other instances, the first regional data center 130 may store a current or updated version of the user-defined code used to execute the task, whereas the second regional data center 130 stores an old or invalid version of the user-defined code.

The user and task history data store 126 may further store data identifying which regional data centers 130 have previously received requests to execute the task and/or have virtual machine instances provisioned to execute the task and which regional data centers 130 are storing a current version of the user-defined code used to execute the requested task (or any version of the user-defined code used to execute the requested task). The server can use this information to then select a regional data center 130 to receive the forwarded task. For example, the server may narrow the selection by considering regional data centers 130 that store a current version of the user-defined code. From this filtered list of regional data centers 130, the server may determine which regional data center(s) 130 have received requests to execute the task and/or have virtual machine instances provisioned to execute the task. If more than one regional data center 130 satisfies this criteria, then the server may select the regional data center 130 from those that satisfy the criteria that is closest to the POP 120. If no regional data center 130 has received requests to execute the task and/or does not have a virtual machine instance provisioned to execute the task, then the server may select the regional data center 130 that is closest to the POP 120. If no regional data center 130 accessible by the POP 120 has a current version of the user-defined code, then the server may determine whether it is necessary for the user-defined code to be current for the task to be executed. For example, the task request may include an indication of what version of the user-defined code to use to execute the task, the minimum version number of the user-defined code that is sufficient to execute the task, and/or whether the version number matters for execution of the task. If it is not necessary for the user-defined code to be current for the task to be executed, then the server may forward the task to the closest regional data center 130 that has previously received requests to execute the task and/or a virtual machine instance provisioned to execute the task (if present) or the closest regional data center 130 if no regional data center 130 has previously received requests to execute the task and/or has a virtual machine instance provisioned to execute the task. If it is necessary for the user-defined code to be current for the task to be executed, then the server may return an exception to the user device 102 indicating that a current version of the user-defined code is not available.

If the server determines to forward the task to a regional data center 130 that is not the closest regional data center 130 (or any other regional data center 130) because the closest regional data center 130 does not have a virtual machine instance provisioned to execute the task, the server may also instruct the closest regional data center 130 (or any other regional data center 130) to provision a virtual machine instance for executing the task. The server may continue to forward future tasks to the non-closest regional data center 130 until the virtual machine instance is provisioned and ready to execute the task. Once the virtual machine instance is provisioned (e.g., once the server is notified by the regional data center 130 that the virtual machine instance is provisioned), then the server may start forwarding future tasks to the closest regional data center 130 (or any other regional data center 130 instructed to provision a virtual machine instance for executing the task).

As described above, the region server 131 receives a task forwarded by the server in the auxiliary services 122. The region server 131 may then forward the task to the on-demand code execution environment 132 for execution. After execution is complete, the on-demand code execution environment 132 forwards the execution results to the region server 131. The region server 131 can then forward the execution results to the server, which then forwards the execution results to the user device 102 that requested the task execution.

As described above, tasks are executed according to user-defined code. A user (using, for example, a user device 102) may compose code for use in executing a task. The user-defined code may be associated with a particular geographic region selected by the user and stored within the regional data center 130 associated with the selected geographic region (e.g., stored within data store 260 of the on-demand code execution environment 132). To enable multiple regional data centers 130 to execute a task, the user can identify one or more other geographic regions that are authorized to execute the task. Alternatively, a default set of geographic regions can be authorized to execute the task. The authorized geographic regions can be stored in the replication data store 135 in the regional data center 130 associated with the user-defined code's geographic region in an entry associated with the user-defined code.

Once other geographic regions are authorized to execute the task, the replication system 133 in the regional data center 130 associated with the user-defined code's geographic region may replicate the bits of the user-defined code and transmit the replicated bits to the replication systems 133 in the regional data centers 130 associated with the authorized geographic regions. The replication system 133 in the regional data center 130 associated with the user-defined code's geographic region may also replicate the bits of the user-defined code and transmit the replicated bits to the replication systems 133 in the regional data centers 130 associated with the authorized geographic regions when the user-defined code is updated. The replication system 133 that replicated the user-defined code and transmitted the replicated bits can periodically request a status of the transfer from the other replication systems 133 that are receiving the replicated bits. Statuses can include a version of the user-defined code currently stored in the regional data center 130, whether the latest replication transmission is complete (and if not, the progress of the transmission), and/or whether the replication transmission has started. The replication system 133 can store the received statuses in the replication data store 135 in an entry associated with the user-defined code. Periodically, the replication system 133 can retrieve the statuses from the replication data store 135 and transmit the statuses to the POP 120 associated with the regional data center 130 for storage in the user and task history data store 126. As described above, the server in the auxiliary services 122 can use this information to select a regional data center 130 to receive a forwarded task.

As an illustrative example, a user may define code that is associated with the same geographic region as the regional data center 130A. Thus, the user-defined code may be stored in the data store 260 of the on-demand code execution environment 132A. A user may identify a geographic region associated with the regional data center 130B as a geographic region that is authorized to execute the task and this information may be stored in the replication data store 135A. Thus, the replication system 133A may replicate the user-defined code and transmit the replicated user-defined code to the replication system 133B. The replication system 133A may periodically request a status of the transfer from the replication system 133B. The received status may also be stored in the replication data store 135A. The replication system 133A may periodically transmit the received status to the POP 120A for storage in the user and task history data store 126A. The server 207A in the auxiliary services 122A can then use the status information to aid in identifying or selecting a regional data center 130 to execute the task.

In some embodiments, the POP 120 can forward the received statuses to other POPs 120 such that some or all POPs 120 are aware of which regional data centers 130 have current user-defined code and which do not. Alternatively, the replication system 133 can forward the statuses to multiple POPs 120 to achieve the same objective.

In further embodiments, one regional data center 130 can forward a task execution request to another regional data center 130 (assuming that the other regional data center 130 is authorized to execute the task). For example, the POPs 120 may not be aware of (or may not have current information of) which regional data centers 130 have previously received task requests and/or which regional data centers 130 have a virtual machine instance provisioned to execute the task. However, the regional data centers 130 may store such information. As an example, the code usage data store 136 may store information indicating which user-defined code has been used by the respective on-demand code execution environment 132 to execute a task in the past, which regional data centers 130 have previously executed a task or provisioned a virtual machine instance to execute a task, and/or which regional data centers 130 have a current version of the user-defined code. The regional data centers 130 may communicate with each other to share such information. If a region server 131 receives a task request and the region server 131 determines that such a request has not been previously received and/or a virtual machine instance is not provisioned to execute the task, the region server 131 may query the code usage data store 136 to determine whether another regional data center 130 has previously received a request to execute the task and/or has provisioned a virtual machine instance to execute the task. If such a regional data center 130 exists, the region server 131 may forward the task to the region server 131 of that regional data center 130. The results of executing the task can be transmitted back to the regional data center 130 that originally received the forwarded task request before being transmitted to the requesting POP 120 and the user device 102.

As an illustrative example, the region server 131A may receive a task request from the POP 120A. However, the on-demand code execution environment 132A may not have a virtual machine instance provisioned to execute the task. The on-demand code execution environment 132B, though, may have a virtual machine instance provisioned to execute the task. Thus, the region server 131A may forward the request to the region server 131B for execution. The region server 131B can then forward the request to the on-demand code execution environment 132B. Results of executing the task may be forwarded by the on-demand code execution environment 132B to the region server 131B, the region server 131B may forward the results to the region server 131A, the region server 131A may forward the results to the server 207A in the auxiliary services 122A, and the server 207A may forward the results to the user device 102 that requested execution of the task.

Various example user devices 102 are shown in FIG. 1, including a desktop computer, laptop, and a mobile phone, each provided by way of illustration. In general, the user devices 102 can be any computing device such as a desktop, laptop or tablet computer, personal computer, wearable computer, server, personal digital assistant (PDA), hybrid PDA/mobile phone, mobile phone, electronic book reader, set-top box, voice command device, camera, digital media player, and the like. The on-demand code execution environments 124 and/or 132 may provide the user devices 102 with one or more user interfaces, command-line interfaces (CLI), application programing interfaces (API), and/or other programmatic interfaces for generating and uploading user-executable code, invoking the user-provided code (e.g., submitting a request to execute the user codes on the on-demand code execution environments 124 and/or 132), scheduling event-based jobs or timed jobs, tracking the user-provided code, and/or viewing other logging or monitoring information related to their requests and/or user codes. Although one or more embodiments may be described herein as using a user interface, it should be appreciated that such embodiments may, additionally or alternatively, use any CLIs, APIs, or other programmatic interfaces.

The network 110 may include any wired network, wireless network, or combination thereof. For example, the network 110 may be a personal area network, local area network, wide area network, over-the-air broadcast network (e.g., for radio or television), cable network, satellite network, cellular telephone network, or combination thereof. As a further example, the network 110 may be a publicly accessible network of linked networks, possibly operated by various distinct parties, such as the Internet. In some embodiments, the network 110 may be a private or semi-private network, such as a corporate or university intranet. The network 110 may include one or more wireless networks, such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Long Term Evolution (LTE) network, or any other type of wireless network. The network 110 can use protocols and components for communicating via the Internet or any of the other aforementioned types of networks. For example, the protocols used by the network 110 may include Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), Message Queue Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and the like. Protocols and components for communicating via the Internet or any of the other aforementioned types of communication networks are well known to those skilled in the art and, thus, are not described in more detail herein.

FIG. 2A illustrates an example block diagram of the auxiliary services 122 of FIG. 1, according to one embodiment. As illustrated in FIG. 2A, the auxiliary services 122 can include a server 207 communicatively coupled to a service data store 208. The server 207 and service data store 208 may operate in conjunction to implement functionalities of the auxiliary services 122. For example, where the auxiliary services 122 is an edge server for a CDN, the server 207 and service data store 208 may operate to cache distributed content (e.g., as provided by a user of the auxiliary services 122) and respond to requests from end users for such cached content. As a further example, where the auxiliary services 122 is a database system, the server 207 and service data store 208 may operate to facilitate and manage interactions with a database. In general, auxiliary services 122 may include any network-based service or data source.

Auxiliary services 122 may be associated with operation of the on-demand code execution environment 124. For example, the server 207 may determine whether a requested task should be executed locally by the on-demand code execution environment 124 or remotely by a regional data center 130 in a manner as described above. If the server 207 determines that a regional data center 130 should receive the task for execution, the server 207 may further determine which regional data center 130 should receive the task in a manner as described above. In some instances, auxiliary services 122 actively transmit information, such as task requests (e.g., in the form of API calls) or other task-triggering information, to the on-demand code execution environment 124. In other instances, auxiliary services 122 may be passive, such that data is made available for access by the on-demand code execution environment 124. For example, components of the on-demand code execution environment 124 may periodically poll such passive data sources, and trigger execution of tasks within the on-demand code execution environment 124 based on the data provided (e.g., based on the availability of a task request received from a user device 102).

Operation of various auxiliary services 122, including CDN networks, database services, data storage services, and data processing services, are known within the art, and therefore will not be described herein. While a simplified view of auxiliary services 122 is shown in FIG. 2A (e.g., including a single server 207 and service data store 208), the POP 120 may implement auxiliary services 122 by use of any number of computing or storage devices, which may not be shown in FIG. 2A. In some instances, computing or storage devices associated with auxiliary services 122 may also be utilized to implement functionalities of the on-demand code execution environment 124. For example, virtual machine instances 250 of the on-demand code execution environment 124 (which are described in more detail below) may be implemented by the server 207. In some instances, the on-demand code execution environment 124 may execute tasks directly on the server 207 (e.g., without use of a virtual machine instance 250).

FIG. 2B illustrates an example block diagram of the on-demand code execution environments 124 and 132 of FIG. 1, according to one embodiment. As illustrated in FIG. 2A, the on-demand code execution environment 124 and the on-demand code execution environment 132 may each include the same components. However, the on-demand code execution environment 124 and the on-demand code execution environment 132 may receive data from different sources.

The on-demand code execution environments 124 and 132 include a frontend 220, worker manager 230, instance pool 240, and data stores 260 collectively configured to enable users (via user devices 102) or regional data centers 130 to submit computer executable instructions (also referred to herein as “code,” “program code,” or “user-defined code”) to the on-demand code execution environment 124 or 132 for execution as a “task.” For example, the data store 260 in the on-demand code execution environment 124 may store a copy of the code defined by a user. The code stored in the on-demand code execution environment 124 may be received from an on-demand code execution environment 132. The data store 260 in the on-demand code execution environment 132 may store a primary copy of the code defined by a user. The code stored in the on-demand code execution environment 132 may be received from a user device 102. Thus, the on-demand code execution environment 132 may store the primary, read-write version of the code, whereas the on-demand code execution environment 124 may store a secondary, read-only version of the code. An update to the code stored in the on-demand code execution environment 132 may be propagated (e.g., by the replication system 133) to other regional data centers 130 and/or POPs 120.

The frontend 220 can facilitate interactions between the on-demand code execution environment 124 and the user devices 102, auxiliary services 122, and/or other computing devices (not shown in FIG. 1 or 2B) via the network 110. Similarly, the frontend 220 can facilitate interactions between the on-demand code execution environment 132 and the region server 131, the user devices 102, and/or other computing devices (not shown in FIG. 1 or 2B) via the network 110. These interactions may include, for example, submission of code, which may be stored within the data stores 260, or transmission of requests to execute code, which may be communicated to the worker manager 230 for assignment to and execution by a virtual machine instance 250 within the instance pool 240. The frontend 220 may receive calls to execute tasks from the server 207 (if in the on-demand code execution environment 124) or the region server 131 (if in the on-demand code execution environment 132) in response to Hypertext Transfer Protocol Secure (HTTPS) requests (e.g., task requests) from a user. Also, any information (e.g., headers and parameters) included in the HTTPS request may also be processed and utilized when executing a task. Any other protocols, including, for example, HTTP, MQTT, and CoAP, may be used to transfer the message containing a task call to the frontend 220.

The on-demand code execution environments 124 and 132 are depicted in FIG. 2B as operating in a distributed computing environment including several computer systems that are interconnected using one or more computer networks (not shown in FIG. 1 or 2B). The on-demand code execution environments 124 and 132 could also operate within a computing environment having a fewer or greater number of devices than are illustrated in FIG. 1 or 2B. Thus, the depiction of the on-demand code execution environments 124 and 132 in FIGS. 1 and 2B should be taken as illustrative and not limiting to the present disclosure. For example, the on-demand code execution environments 124 and 132 or various constituents thereof could implement various Web services components, hosted or “cloud” computing environments, and/or peer to peer network configurations to implement at least a portion of the processes described herein. Further, the on-demand code execution environments 124 and 132 may be implemented directly in hardware or software executed by hardware devices and may, for instance, include one or more physical or virtual servers implemented on physical computer hardware configured to execute computer executable instructions for performing various features that will be described herein. The one or more servers may be geographically dispersed or geographically co-located, for instance, in one or more POPs 120 or regional data centers 130.

The frontend 220 can distribute a request to execute a task to a worker manager 230, which can assign tasks to virtual machine instances 250 for execution. In the example illustrated in FIG. 2B, the worker manager 230 manages the instance pool 240, which is a group (sometimes referred to as a pool) of virtual machine instances 250 that are utilized to execute tasks. As shown in FIG. 2B, instances 250 may have operating systems (OS) 252, language runtimes 254, and containers 256. Containers 256 are logical units created within a computing device using the resources available on that device, and may be utilized to isolate execution of a task from other processes (e.g., task executions) occurring on the device. For example, in order to service a request to execute a task, the worker manager 230 may, based on information specified in the request, create a new container 256 within a virtual machine instance 250 within which to execute the task. In other instances, the worker manager 230 may locate an existing container 256 in one of the instances 250 in the instance pool 240 and assign the container 250 to handle the execution of the task. Containers 256 may be implemented, for example, as Linux containers. The containers 256 may have individual copies of the OSes 252, the runtimes 254, and user-defined code 258 corresponding to various tasks assigned to be executed within the container 256.

While the instance pool 240 is shown in FIG. 2B as a single grouping of virtual machine instances 250, some embodiments of the present application may separate virtual machine instances 250 that are actively assigned to execute tasks from those virtual machine instances 250 that are not actively assigned to execute tasks. For example, those virtual machine instances 250 actively assigned to execute tasks may be grouped into an “active pool,” while those virtual machine instances 250 not actively assigned to execute tasks may be placed within a “warming pool.” Those virtual machine instances 250 within the warming pool may be pre-initialized with an operating system, language runtimes, or other software required to enable rapid execution of tasks in response to user requests. Further details regarding active pools and warming pools are described in greater detail within the '648 application, which is incorporated by reference above (e.g., at FIG. 1 of the '648 application).

On receiving a request to execute a task, the worker manager 230 may locate a virtual machine instance 250 within the instance pool 240 that has available capacity to execute the task. The worker manager 230 may further create a container 256 within the virtual machine instance 250, and provision the container 256 with any software required to execute the task (e.g., an operating system 252, runtime 254, or code 258). For example, a container 256 is shown in FIG. 2B provisioned with operating system 252B, runtime 254B, and a set of code 258. The operating system 252B and runtime 254B may be the same as the operating system 252A and runtime 254A utilized by the virtual machine instance 250, or may be different. After provisioning the container 256 with the requisite software for a task, the worker manager 230 can cause the virtual machine instance 256 to execute the task on behalf of a user.

The worker manager 230 includes a processing unit, a network interface, a computer readable medium drive, and an input/output device interface, all of which may communicate with one another by way of a communication bus. The network interface may provide connectivity to one or more networks or computing systems. The processing unit may thus receive information and instructions from other computing systems or services via the network 110. The processing unit may also communicate to and from a memory of the worker manager 230 and further provide output information for an optional display via the input/output device interface. The input/output device interface may also accept input from an optional input device. The memory may contain computer program instructions (grouped as modules in some embodiments) that the processing unit executes in order to implement one or more aspects of the present disclosure.

Additional details of the components and functionality of the auxiliary services 122 and the on-demand code execution environments 124 and 132 are described in greater detail in the '859 application, which is incorporated by reference above.

Example Block Diagrams for Executing a Task

FIG. 3A is a block diagram of the operating environment 100 of FIG. 1 illustrating the operations performed by the components of the operating environment 100 to execute a task, according to one embodiment. As illustrated in FIG. 3A, the user device 102 calls a task by transmitting a request to execute the task to the server 207 at (1). The server 207 may query the user and task history data store 126 for task data at (2). The task data may include the popularity of the requested task, the historical volume of requests received from the user device 102 that requested the task execution, the latency-sensitivity of the task, properties of the task, and/or the like. The server 207 may then use the task data, and information identifying how busy the POP 120 is, a time it may take to execute the task locally at the POP 120, a time it may take to execute the task within a regional data center 130, the time of day that the request is received, and/or the like to determine whether to execute the task locally at (3). For example, the server 207 may determine whether sufficient computing resources are available to execute the task locally (and may determine to execute the task locally if the POP 120 has sufficient computing resources available). As another example, the server 207 may determine whether requests to execute the task are commonly received (and may determine to execute the task locally if requests to execute the task are common). If the server 207 determines to execute the task locally, then the server 207 may instruct the on-demand code execution environment 124 to execute the task at (4). Otherwise, if the server 207 determines not to execute the task locally, the server 207 may forward the task to the region server 131 at (7).

As described above, the server 207 may forward the task to the region server 131 even if the POP 120 has sufficient computing resource capacity to execute the task. For example, the server 207 may use information stored in the user and task history data store 126 as well as information identifying how busy the POP 120 is, a time it may take to execute the task locally at the POP 120, a time it may take to execute the task within a regional data center 130, the time of day that the request is received, and/or the like to determine whether to execute the task locally or to forward the task to the region server 131.

If the server 207 instructs the on-demand code execution environment 124 to execute the task, then the on-demand code execution environment 124 executes the task at (5). After execution, the on-demand code execution environment 124 reports results of the execution to the server 207 at (6).

If the server 207 forwards the task to the region server 131, the region server 131 then instructs the on-demand code execution environment 132 to execute the task at (8). In response to the instruction, the on-demand code execution environment 132 executes the task at (9) and reports results of the execution to the region server 131 at (10). The region server 131 then reports the execution results to the server 207 at (11).

Once the server 207 receives the execution results (either from the on-demand code execution environment 124 or from the region server 131), then the server 207 reports the execution results to the user device 102 at (12). Thus, the task may be successfully executed regardless of whether the POP 120 in which the server 207 is present has sufficient computing resource capacity to execute the task.

FIG. 3B is another block diagram of the operating environment 100 of FIG. 1 illustrating the operations performed by the components of the operating environment 100 to execute a task, according to one embodiment. As illustrated in FIG. 3B, the user device 102 calls a task by transmitting a request to execute the task to the server 207 at (1). The server 207 may query the user and task history data store 126 for task data at (2). The task data may include the popularity of the requested task, the historical volume of requests received from the user device 102 that requested the task execution, the latency-sensitivity of the task, properties of the task, and/or the like. The server 207 may then use the task data, and information identifying how busy the POP 120 is, a time it may take to execute the task locally at the POP 120, a time it may take to execute the task within a regional data center 130, the time of day that the request is received, and/or the like to determine whether to execute the task locally at (3). For example, the server 207 may determine whether sufficient computing resources are available to execute the task locally (and may determine to execute the task locally if the POP 120 has sufficient computing resources available). As another example, the server 207 may determine whether requests to execute the task are commonly received (and may determine to execute the task locally if requests to execute the task are common). If the server 207 determines to execute the task locally, then the server 207 may instruct the on-demand code execution environment 124 to execute the task at (4). Otherwise, if the server 207 determines not to execute the task locally, the server 207 may select a region server 131 in a particular geographic region to execute the task.

As described above, the server 207 may forward the task to a region server 131 even if the POP 120 has sufficient computing resource capacity to execute the task. For example, the server 207 may use information stored in the user and task history data store 126 as well as information identifying how busy the POP 120 is, a time it may take to execute the task locally at the POP 120, a time it may take to execute the task within a regional data center 130, the time of day that the request is received, and/or the like to determine whether to execute the task locally or whether to make a determination as to which region server 131 should receive a forwarded task.

If the server 207 does not decide to instruct the on-demand code execution environment 124 to execute the task, then the server 207 may query the task history from the user and task history data store 126 at (7). As described herein, the task history may include information identifying which regional data centers 130 have previously received requests to execute the task and/or have provisioned virtual machine instances to execute the task. In an embodiment, the server 207 further queries the user and task history data store 126 to identify which regional data centers 130 have stored a current version of the user-defined code used to execute the task. Using some or all of this information, the server 207 selects a geographic region to execute the task at (8).

Once a geographic region is selected, the server 207 forwards the task to the region server 131 in the selected geographic region at (9). The region server 131 then instructs the on-demand code execution environment 132 to execute the task at (10). In response to the instruction, the on-demand code execution environment 132 executes the task at (11) and reports results of the execution to the region server 131 at (12). The region server 131 then reports the execution results to the server 207 at (13).

Once the server 207 receives the execution results (either from the on-demand code execution environment 124 or from the region server 131), then the server 207 reports the execution results to the user device 102 at (14). Thus, if the POP 120 is unable to execute a task, the server 207 can use gathered metrics to identify a geographic region that can more efficiently execute the task.

Example Block Diagram for Replicating User-Defined Code to Other Regions

FIG. 4 is a block diagram of the operating environment 100 of FIG. 1 illustrating the operations performed by the components of the operating environment 100 to replicate user-defined code to one or more geographic regions, according to one embodiment. As illustrated in FIG. 4, the on-demand code execution environment 132A sends a notification to the replication system 133A indicating that user-defined code is created or updated at (1). For example, the user-defined code may be stored in the data store 260A in the on-demand code execution environment 132A and may be associated with the geographic region in which the regional data center 130A is present. The on-demand code execution environment 132A may monitor for changes to user-defined code or for the creation of new user-defined code at the on-demand code execution environment 132A (e.g., the frontend 220A) may notify the replication system 133A when this occurs so that the replication system 133A can replicate the code to other geographic regions.

Upon receiving the notification, the replication system 133A can retrieve the created or updated user-defined code from the on-demand code execution environment 132A at (2). Once the user-defined code is retrieved, the replication system 133A can replicate the user-defined code at (3). For example, the replication system 133A may replicate the bits of the user-defined code.

After the user-defined code is replicated, the replication system 133A may begin transmitting the replicated user-defined code to one or more other replication systems 133B-N at (4). For example, the replication system 133A may transmit the replicated user-defined code to the other replication systems 133B-N in parallel, in sequence, in bursts or groups, and/or in any combination thereof.

In other embodiments, not shown, the replication system 133A can alternatively or in addition transmit the replicated user-defined code to one or more POPs 120A-N. The POPs 120A-N may receive the replicated user-defined code such that these entities do not have to retrieve the user-defined code from the geographic region in which the code is stored and/or so that the POPs 120A-N can forward the replicated user-defined code to the various regional data centers 130B-N.

Periodically, the replication system 133A may determine a status of the transmissions of the replicated user-defined code. For example, the replication system 133A can transmit a message to any number of the replication systems 133B-N requesting the replication status at (5). The replication status can include a version of the user-defined code currently stored in the regional data center 130B-N, whether the latest replication transmission is complete (and if not, the progress of the transmission), and/or whether the replication transmission has started. The replication system 133A may store the received statuses in the replication data store 135A at (6).

Synchronously with or asynchronously from the replication status queries, the replication system 133A can retrieve the statuses stored in the replication data store 135A at (7) and transmit the statuses to the server 207 at (8). The replication system 133A may forward the statuses to the server 207A to aid the server 207A in making a determination of which geographic region to select to receive a forwarded task.

Example Table in the Replication Data Store

FIG. 5 is an example table 510 stored in the replication data store 135 of FIG. 1, according to one embodiment. The table 510 may be associated with a specific version of user-defined code that is associated with a particular geographic region. Thus, if multiple versions of the same user-defined code exist in a particular geographic region, then the replication data store 135 may include a separate table 510 for each version of the user-defined code.

As illustrated in FIG. 5, the table 510 identifies a name of the user-defined code (e.g., function 515), a current version of the user-defined code (e.g., version 1.6), geographic regions that are authorized to execute the task corresponding to the user-defined code (e.g., geographic regions 520, 522, and 524), the replication status of the user-defined code stored in each of the authorized geographic regions, and a time that each geographic region was last fully synched with the geographic region of the function 515 (e.g., the last time that a particular geographic region received a complete replicated version of the function 515). For example, the status for the geographic region 520 indicates that the user-defined code is fully replicated with the last complete sync occurring at 12:45:32 on Mar. 23, 2016, the status for the geographic region 522 indicates that the replication is in progress and is 50% complete with the last complete sync occurring at 15:02:44 on Mar. 22, 2016, and the status for the geographic region 524 indicates that the replication has not started and the version of the user-defined code stored in the regional data center 130 in the geographic region 524 is version 1.5 with the last complete sync occurring at 22:10:31 on Mar. 19, 2016. The replication data store 135 may include additional tables, one for each user-defined code that is associated with the geographic region in which the replication data store 135 is present and/or one for each version of the user-defined code that is associated with the geographic region in which the replication data store 135 is present.

Example Task Execution Routines

FIG. 6 is a flow diagram depicting a task execution routine 600 illustratively implemented by a POP, according to one embodiment. As an example, the POP 120 (e.g., the server 207) of FIGS. 1 and 2A-2B can be configured to execute the task execution routine 600. The task execution routine 600 begins at block 602.

At block 604, a task is received. For example, the task may be received from a user device 102. The task may be received for the purpose of executing the task.

At block 606, a determination is made as to whether the POP 120 will execute the task locally. For example, the POP 120 determines whether there is a sufficient amount of computing resources available to execute the task locally (and may determine to execute the task locally if the POP 120 has sufficient computing resources available). As another example, the POP 120 determines whether requests to execute the task are commonly received (and may determine to execute the task locally if requests to execute the task are common). If the POP 120 determines to execute the task locally, the task execution routine 600 proceeds to block 608. Otherwise, if the POP 120 determines not to execute the task locally, the task execution routine 600 proceeds to block 612.

At block 608, the task is transmitted to the local code execution environment. For example, the task is transmitted to the on-demand code execution environment 124. The local code execution environment may then use a virtual machine instance to execute the task.

At block 610, an execution result is received from the local code execution environment. The execution result may include content (e.g., media, audio content, video content, etc.), a confirmation that an action corresponding to the task has been performed (e.g., a change to a parameter, the authentication of credentials, etc.), a notification that execution failed, and/or the like.

At block 612, the task is transmitted to a server in a geographic region. For example, the POP 120 transmits the task to the region server 131 in the geographic region closest to the POP 120. The server in the geographic region then forwards the task to a code execution environment local to the geographic region for execution. The results of the execution are then forwarded from the code execution environment local to the geographic region to the server in the geographic region.

At block 614, an execution result is received from the server in the geographic region. Thus, the POP 120 may receive the execution result from the local code execution environment or from a server in a geographic region.

At block 616, the execution result is transmitted. For example, the execution result is transmitted to the user device 102 that initiated the task call. After the execution result is transmitted, the task execution routine 600 may be complete, as shown in block 618.

FIG. 7 is another flow diagram depicting a task execution routine 700 illustratively implemented by a POP, according to one embodiment. As an example, the POP 120 (e.g., the server 207) of FIGS. 1 and 2A-2B can be configured to execute the task execution routine 700. The task execution routine 700 begins at block 702.

At block 704, a task is received. For example, the task may be received from a user device 102. The task may be received for the purpose of executing the task.

At block 706, a determination is made as to whether the POP 120 will execute the task locally. For example, the POP 120 determines whether there is a sufficient amount of computing resources available to execute the task locally (and may determine to execute the task locally if the POP 120 has sufficient computing resources available). As another example, the POP 120 determines whether requests to execute the task are commonly received (and may determine to execute the task locally if requests to execute the task are common). If the POP 120 determines to execute the task locally, the task execution routine 700 proceeds to block 708. Otherwise, if the POP 120 determines not to execute the task locally, the task execution routine 700 proceeds to block 712.

At block 708, the task is transmitted to the local code execution environment. For example, the task is transmitted to the on-demand code execution environment 124. The local code execution environment may then use a virtual machine instance to execute the task.

At block 710, an execution result is received from the local code execution environment. The execution result may include content (e.g., media, audio content, video content, etc.), a confirmation that an action corresponding to the task has been performed (e.g., a change to a parameter, the authentication of credentials, etc.), a notification that execution failed, and/or the like.

At block 712, a geographic region to execute the task is determined. While the default geographic region to execute the task may be the geographic region that is closest to the POP 120, the closest geographic region may not always be the most efficient geographic region to handle the task. For example, the POP 120 may consider which regional data centers 130 are storing a current version of the user-defined code, which regional data centers 130 have previously received a request to execute the task, and/or which regional data centers 130 have provisioned a virtual machine instance to execute the task. The POP 120 can, for example, determine which regional data centers 130 have a current version of the user-defined code stored therein and then select one regional data center 130 from those that have the current version of the user-defined code to execute the task. The POP 120 may consider which of those regional data centers 130 has previously received a request to execute the task or has a virtual machine instance provisioned to execute the task in selecting the appropriate regional data center 130. In further embodiments, the closest or otherwise most-efficient regional data center 130 is not selected because that regional data center 130 does not have a virtual machine instance provisioned to execute the task or otherwise has never previously received a request to execute the task. In such a situation, the POP 120 may instruct the regional data center 130 to provision a virtual machine instance to execute the task so that this regional data center 130 can receive future task execution requests.

At block 714, the task is transmitted to a server in the determined geographic region. The server in the determined geographic region then forwards the task to a code execution environment local to the determined geographic region for execution. The results of the execution are then forwarded from the code execution environment local to the determined geographic region to the server in the determined geographic region.

At block 716, an execution result is received from the server in the determined geographic region. Thus, the POP 120 may receive the execution result from the local code execution environment or from a server in the determined geographic region.

At block 718, the execution result is transmitted. For example, the execution result is transmitted to the user device 102 that initiated the task call. After the execution result is transmitted, the task execution routine 700 may be complete, as shown in block 720.

Terminology

All of the methods and tasks described herein may be performed and fully automated by a computer system. The computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or device (e.g., solid state storage devices, disk drives, etc.). The various functions disclosed herein may be embodied in such program instructions, or may be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located. The results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid state memory chips or magnetic disks, into a different state. In some embodiments, the computer system may be a cloud-based computing system whose processing resources are shared by multiple distinct business entities or other users.

Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described operations or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, operations or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware (e.g., ASICs or FPGA devices), computer software that runs on computer hardware, or combinations of both. Moreover, the various illustrative logical blocks and modules described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor device can be a microprocessor, but in the alternative, the processor device can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor device can include electrical circuitry configured to process computer-executable instructions. In another embodiment, a processor device includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. A processor device can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor device may also include primarily analog components. For example, some or all of the rendering techniques described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few.

The elements of a method, process, routine, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor device, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of a non-transitory computer-readable storage medium. An exemplary storage medium can be coupled to the processor device such that the processor device can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor device. The processor device and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor device and the storage medium can reside as discrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements or steps. Thus, such conditional language is not generally intended to imply that features, elements or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without other input or prompting, whether these features, elements or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to each be present.

While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As can be recognized, certain embodiments described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. The scope of certain embodiments disclosed herein is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. A computer-implemented method comprising: causing a first code execution environment located at a first geographic location to provision a virtual machine instance;receiving a request to execute code stored on a second code execution environment located at a second geographic location different than the first geographic location, wherein the second code execution environment comprises a computing device;determining that the code should be executed on the first code execution environment, wherein the first code execution environment comprises a replication of the code, and wherein the determination that the code should be executed on the first code execution environment is based at least in part on a determination that the first code execution environment has previously received a request to execute the code and the virtual machine instance has been provisioned to execute the replication of the code in the first geographic location of the first code execution environment;transmitting the request to execute the code to the first geographic location of the first code execution environment; andreceiving results of the execution of the replication of the code by the first code execution environment.
2. The computer-implemented method of claim 1, wherein the computing device is at least one of a second virtual machine instance or a physical computing device.
3. The computer-implemented method of claim 1, wherein the first code execution environment comprises the virtual machine instance.
4. The computer-implemented method of claim 1, wherein determining that the code should be executed on the first code execution environment further comprises determining that the code should be executed on the first code execution environment based at least in part on how often the computing device is requested to execute the code, a duration of time to execute the code by the second code execution environment, a duration of time to execute the replication of the code by the first code execution environment, a time of day that the request to execute the code is received, a latency-sensitivity of the code, or properties of the code.
5. The computer-implemented method of claim 1, wherein transmitting the request to execute the code further comprises transmitting the request to execute the code via a second computing device located at the first geographic location of the first code execution environment.
6. The computer-implemented method of claim 1, further comprising: receiving a second request to execute second code on the second code execution environment;determining that the second code should be executed on the second code execution environment;transmitting the second request to execute the second code to the second code execution environment; andreceiving second results of execution of the second code by the second code execution environment.
7. The computer-implemented method of claim 6, wherein determining that the second code should be executed on the second code execution environment further comprises determining that the second code should be executed on the second code execution environment based on an availability of computing resources of the computing device.
8. A system comprising: a first code execution environment that comprises a first computing device and that is located at a first geographic location; anda second computing device comprising a processor in communication with the first code execution environment and configured with specific computer-executable instructions to: cause a second code execution environment located at a second geographic location different than the first geographic location to provision a virtual machine instance;receive a request to execute code;determine that the code should be executed on the second code execution environment, wherein the second code execution environment comprises a replication of the code, and wherein the determination that the code should be executed on the second code execution environment is based at least in part on a determination that the virtual machine instance has been provisioned to execute the replication of the code in the second geographic location of the second code execution environment;transmit the request to execute the code to the second geographic location of the second execution environment; andreceive results of execution of the replication of the code by the second code execution environment.
9. The system of claim 8, wherein the first computing device is at least one of a second virtual machine instance or a physical computing device.
10. The system of claim 8, wherein the second code execution environment comprises the virtual machine instance.
11. The system of claim 8, wherein the second computing device is further configured with specific computer-executable instructions to determine that the code should be executed on the second code execution environment based at least in part on at least one of how often the system is requested to execute the code, a duration of time to execute the code by the first code execution environment, a duration of time to execute the replication of the code by the second code execution environment, a time of day that the request to execute the code is received, a latency-sensitivity of the code, or properties of the code.
12. The system of claim 8, wherein the second computing device is further configured with specific computer-executable instructions to transmit the request to execute the code to the second code execution environment via a third computing device located at the second geographic location of the second code execution environment.
13. The system of claim 8, wherein the second computing device is further configured with specific computer-executable instructions to: receive a second request to execute second code on the first code execution environment;determine that the second code should be executed on the first code execution environment;transmit the second request to execute the second code to the first code execution environment; andreceive second results of execution of the second code by the first code execution environment.
14. The system of claim 13, wherein the second computing device is further configured with specific computer-executable instructions to determine that the second code should be executed on the first code execution environment based on an availability of computing resources of the system to execute the second code.
15. Non-transitory, computer-readable storage media comprising computer-executable instructions for selecting a location at which to execute code, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to: cause a first code execution environment located at a first geographic location to provision a virtual machine instance;receive a request to execute code on a second code execution environment located at a second geographic location different than the first geographic location;determine that the code should be executed on the first code execution environment, wherein the first code execution environment comprises a replication of the code, and wherein the determination that the code should be executed on the first code execution environment is based at least in part on a determination that the virtual machine instance has been provisioned to execute the replication of the code in the first geographic location of the first code execution environment;transmit the request to execute the code to the geographic location of the first code execution environment; andreceive results of execution of the replication of the code by the first code execution environment.
16. The non-transitory, computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the computer system to transmit the results to a user device.
17. The non-transitory, computer-readable storage media of claim 15, wherein the first code execution environment comprises the virtual machine instance.
18. The non-transitory, computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the computer system to determine that the code should be executed on the first code execution environment based on at least one of how often the computer system is requested to execute the code, a duration of time to execute the code by the second code execution environment, a duration of time to execute the replication of the code by the first code execution environment, a time of day that the request to execute the code is received, a latency-sensitivity of the code, or properties of the code.
19. The non-transitory, computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the computer system to: receive a second request to execute second code on the second code execution environment;determine that the second code should be executed on the second code execution environment;transmit the second request to execute the second code to the second code execution environment; andreceive second results of execution of the second code by the second code execution environment.
20. The non-transitory, computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the computer system to determine that the second code should be executed on the second code execution environment based on an availability of computing resources of the computer system to execute the second code.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/391,673, entitled “MULTI-REGION REQUEST-DRIVEN CODE EXECUTION SYSTEM” and filed on Dec. 27, 2016, which is hereby incorporated by reference herein in its entirety.

US Referenced Citations (1801)

Number	Name	Date	Kind
1075551	D'Amours	Oct 1913	A
5063500	Shorter	Nov 1991	A
5341477	Pitkin et al.	Aug 1994	A
5459837	Caccavale	Oct 1995	A
5611049	Pitts	Mar 1997	A
5627889	Eslambolchi	Mar 1997	A
5701467	Freeston	Sep 1997	A
5764910	Shachar	Jun 1998	A
5774660	Brendel et al.	Jun 1998	A
5815649	Utter et al.	Sep 1998	A
5852717	Bhide et al.	Nov 1998	A
5892914	Pitts	Apr 1999	A
5893116	Simmonds et al.	Apr 1999	A
5895462	Toki	Apr 1999	A
5905248	Russell et al.	Apr 1999	A
5933811	Angles et al.	May 1999	A
5937427	Shinagawa et al.	Aug 1999	A
5974454	Apfel et al.	Aug 1999	A
5991306	Burns et al.	Oct 1999	A
5991809	Kriegsman	Nov 1999	A
5999274	Lee et al.	Nov 1999	A
6006264	Colby et al.	Dec 1999	A
6016512	Huitema	Jan 2000	A
6026452	Pitts	Jan 2000	A
6085234	Pitts et al.	Jan 2000	A
6038601	Lambert et al.	Feb 2000	A
6052718	Gifford	Mar 2000	A
6078960	Ballard	Apr 2000	A
6018619	Allard et al.	Jun 2000	A
6092100	Berstis et al.	Jul 2000	A
6098096	Tsirigotis et al.	Jul 2000	A
6108703	Leighton et al.	Aug 2000	A
6128279	O'Neil et al.	Aug 2000	A
6151631	Ansell et al.	Oct 2000	A
6157942	Chu et al.	Nov 2000	A
6167438	Yates et al.	Dec 2000	A
6167446	Lister et al.	Dec 2000	A
6173316	De Boor et al.	Jan 2001	B1
6178160	Bolton et al.	Jan 2001	B1
6182111	Inohara et al.	Jan 2001	B1
6182125	Borella et al.	Jan 2001	B1
6185598	Farber et al.	Jan 2001	B1
6192051	Lipman et al.	Feb 2001	B1
6205475	Pitts	Feb 2001	B1
6223288	Byrne	Mar 2001	B1
6223209	Watson et al.	Apr 2001	B1
6243761	Mogul et al.	Apr 2001	B1
6275496	Burns et al.	Jun 2001	B1
6256671	Strentzsch et al.	Jul 2001	B1
6286043	Cuomo et al.	Aug 2001	B1
6286084	Wexler et al.	Sep 2001	B1
6304913	Rune	Sep 2001	B1
6324580	Jindal et al.	Oct 2001	B1
6330602	Law et al.	Nov 2001	B1
6338082	Schneider	Jan 2002	B1
6345308	Abe	Jan 2002	B1
6351743	DeArdo et al.	Feb 2002	B1
6351775	Yu	Feb 2002	B1
6363411	Dugan et al.	Feb 2002	B1
6366952	Pitts	Mar 2002	B2
6374290	Scharber et al.	Apr 2002	B1
6377257	Borrel et al.	Apr 2002	B1
6386043	Millins	Apr 2002	B1
6389532	Gupta et al.	May 2002	B1
6405252	Gupta et al.	May 2002	B1
6408360	Chamberlain et al.	Jun 2002	B1
6411967	Van Renesse	Jun 2002	B1
6415280	Farber et al.	Jun 2002	B1
6430607	Kavner	Jul 2002	B1
6438592	Killian	Aug 2002	B1
6442165	Sitaraman et al.	Aug 2002	B1
6452925	Sistanizadeh et al.	Aug 2002	B1
6457047	Chandra et al.	Sep 2002	B1
6459909	Bilcliff et al.	Sep 2002	B1
6473804	Kaiser et al.	Oct 2002	B1
6484143	Swildens et al.	Oct 2002	B1
6484161	Chipalkatti et al.	Nov 2002	B1
6493765	Cunningham et al.	Nov 2002	B1
6505241	Pitts	Jan 2003	B2
6513112	Craig et al.	Jan 2003	B1
6523036	Hickman et al.	Jan 2003	B1
6529910	Fleskes	Feb 2003	B1
6529953	Van Renesse	Mar 2003	B1
6553413	Leighton et al.	Mar 2003	B1
6560610	Eatherton et al.	Apr 2003	B1
6564380	Murphy	May 2003	B1
6611873	Kanehara	May 2003	B1
6622168	Datta	Aug 2003	B1
6633324	Stephens, Jr.	Sep 2003	B2
6643357	Lumsden	Oct 2003	B2
6643707	Booth	Nov 2003	B1
6654807	Farber et al.	Nov 2003	B2
6658462	Dutta	Nov 2003	B1
6665706	Kenner et al.	Dec 2003	B2
6678717	Schneider	Jan 2004	B1
6678791	Jacobs et al.	Jan 2004	B1
6681282	Golden et al.	Jan 2004	B1
6687846	Adrangi et al.	Jan 2004	B1
6694358	Swildens et al.	Feb 2004	B1
6697805	Choquier et al.	Feb 2004	B1
6718324	Edlund et al.	Mar 2004	B2
6724770	Van Renesse	Apr 2004	B1
6732237	Jacobs et al.	Apr 2004	B1
6754699	Swildens et al.	May 2004	B2
6754706	Swildens et al.	Jun 2004	B1
6760721	Chasen et al.	Jun 2004	B1
6769031	Bero	Jul 2004	B1
6782398	Bahl	Jul 2004	B1
6785704	McCanne	Aug 2004	B1
6795434	Kumar et al.	Aug 2004	B1
6799214	Li	Sep 2004	B1
6804706	Pitts	Sep 2004	B2
6810291	Card et al.	Oct 2004	B2
6810411	Coughlin et al.	Oct 2004	B1
6829654	Jungck	Nov 2004	B1
6862607	Vermeulen	Mar 2005	B1
6868439	Basu et al.	Mar 2005	B2
6874017	Inoue et al.	Mar 2005	B1
6917951	Orbits et al.	Mar 2005	B2
6925499	Chen et al.	Jul 2005	B1
6928467	Peng et al.	Aug 2005	B2
6928485	Krishnamurthy et al.	Aug 2005	B1
6941562	Gao et al.	Aug 2005	B2
6944167	McPherson	Sep 2005	B1
6950848	Yousefi'zadeh et al.	Sep 2005	B1
6961783	Cook et al.	Sep 2005	B1
6963850	Bezos et al.	Nov 2005	B1
6968389	Menditto	Nov 2005	B1
6976090	Ben-Shaul et al.	Dec 2005	B2
6981017	Kasriel et al.	Dec 2005	B1
6981025	Frazier et al.	Dec 2005	B1
6985945	Farhat et al.	Jan 2006	B2
6986018	O'Rourke et al.	Jan 2006	B2
6990526	Zhu	Jan 2006	B1
6996616	Leighton et al.	Jan 2006	B1
7003555	Jungck	Feb 2006	B1
7006099	Gut et al.	Feb 2006	B2
7007089	Freedman	Feb 2006	B2
7010578	Lewin et al.	Mar 2006	B1
7010598	Sitaraman et al.	Mar 2006	B2
7024466	Outten et al.	Apr 2006	B2
7027582	Khello et al.	Apr 2006	B2
7031445	Lumsden	Apr 2006	B2
7032010	Swildens et al.	Apr 2006	B1
7058633	Gnagy et al.	Apr 2006	B1
7058706	Iyer et al.	Jun 2006	B1
7058953	Willard et al.	Jun 2006	B2
7062158	Ayaki	Jun 2006	B1
7065587	Huitema et al.	Jun 2006	B2
7072982	Teodosiu et al.	Jun 2006	B2
7076633	Tormasov et al.	Jul 2006	B2
7082476	Cohen et al.	Jul 2006	B1
7086061	Joshi et al.	Aug 2006	B1
7092505	Allison et al.	Aug 2006	B2
7092997	Kasriel et al.	Aug 2006	B1
7095715	Buckman et al.	Aug 2006	B2
7096266	Lewin et al.	Aug 2006	B2
7099936	Chase et al.	Aug 2006	B2
7103645	Leighton et al.	Aug 2006	B2
7114160	Suryanarayana et al.	Sep 2006	B2
7117262	Bai et al.	Sep 2006	B2
7133905	Dilley et al.	Oct 2006	B2
7136922	Sundaram et al.	Nov 2006	B2
7139808	Anderson et al.	Nov 2006	B2
7139821	Shah et al.	Nov 2006	B1
7143169	Champagne et al.	Nov 2006	B1
7143170	Swildens et al.	Nov 2006	B2
7146560	Dang et al.	Nov 2006	B2
7149747	Cheng et al.	Dec 2006	B1
7149809	Barde et al.	Dec 2006	B2
7152118	Anderson, IV et al.	Dec 2006	B2
7162539	Garcie-Luna-Aceves	Jan 2007	B2
7165117	Sitaraman et al.	Jan 2007	B1
7171469	Ackaouy et al.	Jan 2007	B2
7174382	Ramanathan et al.	Jan 2007	B2
7185046	Ferstl et al.	Feb 2007	B2
7185063	Kasriel et al.	Feb 2007	B1
7185084	Sirivara et al.	Feb 2007	B2
7188214	Kasriel et al.	Feb 2007	B1
7194522	Swildens et al.	Mar 2007	B1
7194552	Schneider	Mar 2007	B1
7200667	Teodosiu et al.	Mar 2007	B2
7200673	Augart	Apr 2007	B1
7216170	Ludvig et al.	Apr 2007	B2
7225254	Swildens et al.	May 2007	B1
7228350	Hong et al.	May 2007	B2
7228359	Monteiro	Jun 2007	B1
7233978	Overton et al.	Jun 2007	B2
7240100	Wein et al.	Jun 2007	B1
7249196	Peiffer et al.	Jul 2007	B1
7251675	Kamakura et al.	Jul 2007	B1
7254626	Kommula et al.	Jul 2007	B1
7272227	Beran	Jul 2007	B1
7254634	Davis et al.	Aug 2007	B1
7254636	O'Toole, Jr. et al.	Aug 2007	B1
7257581	Steele et al.	Aug 2007	B1
7260598	Liskov et al.	Aug 2007	B1
7260639	Afergan et al.	Aug 2007	B2
7269784	Kasriel et al.	Sep 2007	B1
7274658	Bornstein et al.	Sep 2007	B2
7284056	Ramig	Sep 2007	B2
7289519	Liskov	Oct 2007	B1
7293093	Leighton	Oct 2007	B2
7302608	Acharya	Nov 2007	B1
7308499	Chavez	Nov 2007	B2
7310686	Uysal	Dec 2007	B2
7316648	Kelly et al.	Jan 2008	B2
7318074	Iyengar et al.	Jan 2008	B2
7320131	O'Toole, Jr.	Jan 2008	B1
7321918	Burd et al.	Jan 2008	B2
7337968	Wilz, Sr. et al.	Jan 2008	B2
7339937	Mitra et al.	Mar 2008	B2
7340505	Lisiecki et al.	Mar 2008	B2
7343397	Kochanski	Mar 2008	B2
7350075	Eastham et al.	Mar 2008	B1
7362703	Taft et al.	Mar 2008	B1
7363291	Page	Apr 2008	B1
7363626	Koutharapu et al.	Apr 2008	B2
7370089	Boyd et al.	Apr 2008	B2
7372809	Chen	May 2008	B2
7373416	Kagan et al.	May 2008	B2
7376716	Dilley et al.	May 2008	B2
7376736	Sundaram et al.	May 2008	B2
7380078	Ikegaya et al.	May 2008	B2
7389354	Sitaraman et al.	May 2008	B1
7392236	Rusch et al.	Jun 2008	B2
7398301	Hennessey et al.	Jun 2008	B2
7406512	Swildens et al.	Jul 2008	B2
7406522	Riddle	Jul 2008	B2
7409712	Brooks et al.	Jul 2008	B1
7430610	Pace et al.	Aug 2008	B2
7441045	Skene et al.	Sep 2008	B2
7441261	Slater et al.	Oct 2008	B2
7451230	Corrado et al.	Oct 2008	B2
7454457	Lowery et al.	Nov 2008	B1
7454500	Hsu et al.	Nov 2008	B1
7461170	Taylor et al.	Nov 2008	B1
7464142	Flurry et al.	Dec 2008	B2
7472201	Aitken	Dec 2008	B1
7478148	Neerdaels	Jan 2009	B2
7492720	Pruthi et al.	Jan 2009	B2
7496651	Joshi	Feb 2009	B1
7499998	Toebes et al.	Feb 2009	B2
7502836	Menditto et al.	Mar 2009	B1
7505464	Okmianski et al.	Mar 2009	B2
7506034	Coates et al.	Mar 2009	B2
7519705	Papagiannaki et al.	Mar 2009	B1
7519720	Fishman et al.	Apr 2009	B2
7519726	Palliyil et al.	Apr 2009	B2
7523181	Swildens et al.	Apr 2009	B2
7543024	Holstege	Apr 2009	B2
7548947	Kasriel et al.	Jun 2009	B2
7552235	Chase et al.	Jun 2009	B2
7555542	Ayers et al.	Jun 2009	B1
7561571	Lovett et al.	Jun 2009	B1
7565407	Hayball	Jul 2009	B1
7568032	Feng et al.	Jul 2009	B2
7573916	Bechtolsheim et al.	Jul 2009	B1
7574499	Swildens et al.	Aug 2009	B1
7581009	Hsu et al.	Aug 2009	B1
7593935	Sullivan	Aug 2009	B2
7584507	Nucci	Sep 2009	B1
7594189	Walker et al.	Sep 2009	B1
7596619	Leighton et al.	Sep 2009	B2
7603439	Dilley et al.	Sep 2009	B2
7613815	Prakash et al.	Oct 2009	B1
7617222	Coulthard et al.	Nov 2009	B2
7623460	Miyazaki	Nov 2009	B2
7624169	Lisiecki et al.	Nov 2009	B2
7624264	Aura et al.	Nov 2009	B2
7631101	Sullivan et al.	Nov 2009	B2
7626940	Jain	Dec 2009	B2
7640296	Fuchs et al.	Dec 2009	B2
7650376	Blumenau	Jan 2010	B1
7653689	Champagne et al.	Jan 2010	B1
7653700	Bahl et al.	Jan 2010	B1
7653725	Yahiro et al.	Jan 2010	B2
7657613	Hanson et al.	Jan 2010	B1
7657622	Douglis et al.	Feb 2010	B1
7661027	Langen et al.	Feb 2010	B2
7664831	Cartmell et al.	Feb 2010	B2
7664879	Chan et al.	Feb 2010	B2
7676570	Levy et al.	Feb 2010	B2
7680897	Carter et al.	Mar 2010	B1
7684394	Cutbill et al.	Mar 2010	B1
7685109	Ransil et al.	Mar 2010	B1
7685251	Houlihan et al.	Mar 2010	B2
7685270	Vermeulen et al.	Mar 2010	B1
7685273	Anastas et al.	Mar 2010	B1
7693813	Cao et al.	Mar 2010	B1
7693959	Leighton et al.	Apr 2010	B2
7698418	Shimada et al.	Apr 2010	B2
7702724	Brydon et al.	Apr 2010	B1
7706740	Collins et al.	Apr 2010	B2
7707071	Rigole	Apr 2010	B2
7707314	McCarthy et al.	Apr 2010	B2
7711647	Gunaseelan et al.	Apr 2010	B2
7711788	Lev Ran et al.	May 2010	B2
7716367	Leighton et al.	May 2010	B1
7725602	Liu et al.	May 2010	B2
7725658	Lang et al.	May 2010	B2
7730187	Raciborski et al.	May 2010	B2
7739400	Lindbo et al.	Jun 2010	B2
7747720	Toebes et al.	Jun 2010	B2
7748005	Romero et al.	Jun 2010	B2
7756017	Goyal et al.	Jul 2010	B2
7756032	Feick et al.	Jul 2010	B2
7756913	Day	Jul 2010	B1
7756965	Joshi	Jul 2010	B2
7757202	Dahlsted et al.	Jul 2010	B2
7761572	Auerbach	Jul 2010	B1
7765295	Anastas et al.	Jul 2010	B2
7765304	Davis et al.	Jul 2010	B2
7769823	Jenny et al.	Jul 2010	B2
7773596	Marques	Aug 2010	B1
7774342	Virdy	Aug 2010	B1
7783727	Foley et al.	Aug 2010	B1
7787380	Aggarwal et al.	Aug 2010	B1
7792989	Toebes et al.	Aug 2010	B2
7805516	Kettler et al.	Sep 2010	B2
7809597	Das et al.	Sep 2010	B2
7813308	Reddy et al.	Oct 2010	B2
7814229	Cabrera et al.	Oct 2010	B1
7818454	Kim et al.	Oct 2010	B2
7827256	Phillips et al.	Oct 2010	B2
7836177	Kasriel et al.	Nov 2010	B2
7853719	Cao et al.	Nov 2010	B1
7853680	Phatak	Dec 2010	B2
7860735	Evanitsky	Dec 2010	B2
7865594	Baumback et al.	Jan 2011	B1
7865953	Hsieh et al.	Jan 2011	B1
7873065	Mukerji et al.	Jan 2011	B1
7890612	Todd et al.	Jan 2011	B2
7890989	Hofrichter et al.	Feb 2011	B1
7899899	Joshi	Feb 2011	B2
7904875	Hegyi	Mar 2011	B2
7912921	O'Rourke et al.	Mar 2011	B2
7925782	Sivasubramanian et al.	Mar 2011	B2
7925713	Day et al.	Apr 2011	B1
7930393	Baumback et al.	Apr 2011	B1
7930402	Swildens et al.	Apr 2011	B2
7930427	Josefsberg et al.	Apr 2011	B2
7933988	Nasuto et al.	Apr 2011	B2
7937456	McGrath	Apr 2011	B2
7937477	Day et al.	May 2011	B1
7945693	Farber et al.	May 2011	B2
7949779	Farber et al.	May 2011	B2
7949785	Alkhatib et al.	May 2011	B2
7958222	Pruitt et al.	May 2011	B1
7958258	Yeung et al.	Jun 2011	B2
7961736	Ayyagari	Jun 2011	B2
7962597	Richardson et al.	Jun 2011	B2
7966404	Hedin et al.	Jun 2011	B2
7970816	Chess et al.	Jun 2011	B2
7970940	van de Ven et al.	Jun 2011	B1
7979509	Malmskog et al.	Jun 2011	B1
7991910	Richardson et al.	Jul 2011	B2
7996404	Wong et al.	Aug 2011	B2
7996533	Leighton et al.	Aug 2011	B2
7996535	Auerbach	Aug 2011	B2
8000724	Rayburn et al.	Aug 2011	B1
8001187	Stochosky	Aug 2011	B2
8010705	Sebastian et al.	Aug 2011	B1
8010707	Elzur et al.	Aug 2011	B2
8019869	Kriegsman	Aug 2011	B2
8024441	Kommula et al.	Sep 2011	B2
8028090	Richardson et al.	Sep 2011	B2
8041773	Abu-Ghazaleh et al.	Sep 2011	B2
8041809	Sundaram et al.	Oct 2011	B2
8041818	Gupta et al.	Oct 2011	B2
8042054	White et al.	Oct 2011	B2
8051166	Baumback et al.	Oct 2011	B1
8065275	Eriksen et al.	Nov 2011	B2
8069231	Schran et al.	Nov 2011	B2
8073940	Richardson et al.	Nov 2011	B1
8079087	Spies et al.	Dec 2011	B1
8082348	Averbuj et al.	Dec 2011	B1
8099487	Smirnov et al.	Jan 2012	B1
8108623	Krishnaprasad et al.	Jan 2012	B2
8117306	Baumback et al.	Jan 2012	B1
8122098	Richardson et al.	Feb 2012	B1
8122124	Baumback et al.	Feb 2012	B1
8132242	Wu	Feb 2012	B1
8135820	Richardson et al.	Mar 2012	B2
8155126	Mao et al.	Mar 2012	B1
8156199	Hoche-Mong et al.	Apr 2012	B1
8156243	Richardson et al.	Apr 2012	B2
8161184	Sekar et al.	Apr 2012	B2
8165915	Lucash	Apr 2012	B1
8175863	Ostermeyer et al.	Apr 2012	B1
8180720	Kovacs et al.	May 2012	B1
8190682	Paterson-Jones et al.	May 2012	B2
8195605	Chellappa et al.	May 2012	B2
8195837	McCarthy et al.	Jun 2012	B2
8209695	Pruyne et al.	Jun 2012	B1
8224971	Miller et al.	Jun 2012	B1
8218965	Uhlhorn et al.	Jul 2012	B1
8219647	Harvell et al.	Jul 2012	B2
8224942	Presotto et al.	Jul 2012	B1
8224986	Liskov et al.	Jul 2012	B1
8224994	Schneider	Jul 2012	B1
8234403	Richardson et al.	Jul 2012	B2
8239530	Sundaram et al.	Jul 2012	B2
8250135	Driesen et al.	Aug 2012	B2
8250211	Swildens et al.	Aug 2012	B2
8250219	Raciborski et al.	Aug 2012	B2
8260914	Ranjan	Aug 2012	B1
8261062	Aura et al.	Sep 2012	B2
8266288	Banerjee et al.	Sep 2012	B2
8266327	Kumar et al.	Sep 2012	B2
8271471	Kamvar et al.	Sep 2012	B1
8280998	Joshi	Sep 2012	B2
8281035	Farber et al.	Oct 2012	B2
8286176	Baumback et al.	Oct 2012	B1
8291046	Farber et al.	Oct 2012	B2
8291117	Eggleston et al.	Oct 2012	B1
8296375	Katzer et al.	Oct 2012	B1
8296393	Alexander et al.	Oct 2012	B2
8296429	Baumback et al.	Oct 2012	B2
8296786	Faust et al.	Oct 2012	B2
8301600	Helmick et al.	Oct 2012	B1
8301645	Crook	Oct 2012	B1
8316124	Baumback et al.	Oct 2012	B1
8321568	Sivasubramanian et al.	Nov 2012	B2
8321588	Richardson et al.	Nov 2012	B2
8331370	Hamilton et al.	Nov 2012	B2
8341745	Chat et al.	Dec 2012	B1
8356074	Ehrlich et al.	Jan 2013	B1
8380831	Barber	Jan 2013	B2
8380851	McCarthy et al.	Feb 2013	B2
8392928	Forys et al.	Feb 2013	B1
8396908	Moore et al.	Mar 2013	B2
8402137	Sivasuramanian et al.	Mar 2013	B2
8423408	Barnes et al.	Mar 2013	B1
8423662	Weihl et al.	Apr 2013	B1
8433749	Wee et al.	Apr 2013	B2
8443167	Fallone et al.	Apr 2013	B1
8447831	Sivasubramanian et al.	May 2013	B1
8447854	Jasinskyj	May 2013	B1
8447876	Verma et al.	May 2013	B2
8452745	Ramakrishna	May 2013	B2
8452870	Baumback et al.	May 2013	B2
8452874	MacCarthaigh et al.	May 2013	B2
8463877	Richardson	May 2013	B1
8458360	Richardson et al.	Jun 2013	B2
8468222	Sakata et al.	Jun 2013	B2
8468245	Farber et al.	Jun 2013	B2
8473613	Farber et al.	Jun 2013	B2
8478903	Farber et al.	Jun 2013	B2
8478883	Day et al.	Jul 2013	B2
8489737	Baumback et al.	Jul 2013	B2
8504721	Hsu et al.	Jul 2013	B2
8504775	Plamondon	Aug 2013	B2
8510428	Joshi	Aug 2013	B2
8510807	Elazary et al.	Aug 2013	B1
8516082	Cadwell et al.	Aug 2013	B2
8521851	Richardson et al.	Aug 2013	B1
8521876	Goodman et al.	Aug 2013	B2
8521880	Richardson et al.	Aug 2013	B1
8521885	Richardson et al.	Aug 2013	B1
8521908	Holmes et al.	Aug 2013	B2
8526405	Curtis et al.	Aug 2013	B2
8527639	Liskov et al.	Sep 2013	B1
8527645	Proffit et al.	Sep 2013	B1
8527658	Holmes et al.	Sep 2013	B2
8549646	Stavrou et al.	Sep 2013	B2
8572208	Farber et al.	Oct 2013	B2
8572210	Farber et al.	Oct 2013	B2
8577992	Richardson et al.	Oct 2013	B1
8577963	Trahan et al.	Nov 2013	B2
8589996	Ma et al.	Nov 2013	B2
8606996	Richardson et al.	Nov 2013	B2
8606926	Levitch	Dec 2013	B2
8612565	Schneider	Dec 2013	B2
8612588	Ehrlich et al.	Dec 2013	B1
8615549	Knowles et al.	Dec 2013	B2
8619780	Brandwine	Dec 2013	B1
8626950	Richardson et al.	Jan 2014	B1
8635340	Schneider	Jan 2014	B1
8639817	Sivasubramanian et al.	Jan 2014	B2
8645539	McCarthy et al.	Jan 2014	B2
8645700	Smith et al.	Feb 2014	B2
8667127	Bettis et al.	Feb 2014	B2
8676918	Richardson et al.	Mar 2014	B2
8683023	Brandwine et al.	Mar 2014	B1
8683076	Farber et al.	Mar 2014	B2
8688837	Richardson et al.	Mar 2014	B1
8694642	Dempsky et al.	Apr 2014	B2
8712950	Smith et al.	Apr 2014	B2
8732309	Richardson et al.	Apr 2014	B1
8738766	Kazerani et al.	May 2014	B1
8745177	Kazerani et al.	May 2014	B1
8756322	Lynch	Jun 2014	B1
8756325	Sivasubramanian et al.	Jun 2014	B2
8756341	Richardson et al.	Jun 2014	B1
8762526	Baumback et al.	Jun 2014	B2
8775553	Cansino et al.	Jun 2014	B2
8782207	Qiu et al.	Jul 2014	B2
8782236	Marshall et al.	Jul 2014	B1
8782279	Eggleston et al.	Jul 2014	B2
8788671	Richardson et al.	Jul 2014	B2
8812727	Sorenson, III et al.	Jul 2014	B1
8819187	Hofmann	Aug 2014	B1
8819283	Richardson et al.	Aug 2014	B2
8826032	Yahalom et al.	Aug 2014	B1
8843625	Baumback et al.	Sep 2014	B2
8902897	Hamilton et al.	Sep 2014	B2
8885584	Praveenkumar et al.	Nov 2014	B2
8904009	Marshall et al.	Dec 2014	B1
8914514	Jenkins et al.	Dec 2014	B1
8914626	Adogla et al.	Dec 2014	B1
8914797	Osogami et al.	Dec 2014	B2
8914814	Middleton et al.	Dec 2014	B1
8924466	Seed et al.	Dec 2014	B2
8924528	Richardson et al.	Dec 2014	B1
8930513	Richardson et al.	Jan 2015	B1
8930544	Richardson et al.	Jan 2015	B2
8935744	Osterweil et al.	Jan 2015	B2
8938526	Richardson et al.	Jan 2015	B1
8949161	Borst et al.	Jan 2015	B2
8949459	Scholl	Feb 2015	B1
8966318	Shah	Feb 2015	B1
8971328	Judge et al.	Feb 2015	B2
8972580	Fleischman et al.	Mar 2015	B2
8976711	Li et al.	Mar 2015	B2
9003035	Richardson et al.	Mar 2015	B1
9003040	MacCarthaigh et al.	Apr 2015	B2
9009286	Sivasubramanian et al.	Apr 2015	B2
9009334	Jenkins et al.	Apr 2015	B1
9021127	Richardson et al.	Apr 2015	B2
9021128	Sivasubramanian et al.	Apr 2015	B2
9021129	Richardson et al.	Apr 2015	B2
9026616	Sivasubramanian et al.	Apr 2015	B2
9037975	Taylor et al.	May 2015	B1
9071502	Baumback et al.	May 2015	B2
9075777	Pope et al.	Jun 2015	B1
9075893	Jenkins	Jul 2015	B1
9083675	Richardson et al.	Jul 2015	B2
9083743	Patel et al.	Jul 2015	B1
9088460	Baumback et al.	Jul 2015	B2
9092141	Hayashi	Jul 2015	B2
9106701	Richardson et al.	Jul 2015	B2
9116803	Agrawal et al.	Aug 2015	B1
9118543	Baumback et al.	Aug 2015	B2
9118680	Dunlap et al.	Aug 2015	B1
9130756	Richardson et al.	Aug 2015	B2
9130977	Zisapel et al.	Sep 2015	B2
9137210	Joglekar et al.	Sep 2015	B1
9137301	Dunlap	Sep 2015	B1
9137302	Makhijani et al.	Sep 2015	B1
9154551	Watson	Sep 2015	B1
9160641	Baumback et al.	Oct 2015	B2
9160703	Richardson et al.	Oct 2015	B2
9172674	Patel et al.	Oct 2015	B1
9176894	Marshall et al.	Oct 2015	B2
9185012	Richardson et al.	Nov 2015	B2
9191338	Richardson et al.	Nov 2015	B2
9191393	Tovar	Nov 2015	B2
9191458	Richardson et al.	Nov 2015	B2
9195996	Walsh et al.	Nov 2015	B1
9208097	Richardson et al.	Nov 2015	B2
9210099	Baumback et al.	Dec 2015	B2
9210235	Sivasubramanian et al.	Dec 2015	B2
9219686	Hilt et al.	Dec 2015	B2
9237087	Risbood et al.	Jan 2016	B1
9237114	Richardson et al.	Jan 2016	B2
9240954	Ellsworth et al.	Jan 2016	B1
9246776	Ellsworth et al.	Jan 2016	B2
9253065	Richardson et al.	Jan 2016	B2
9251112	Richardson et al.	Feb 2016	B2
9276812	Nagargadde et al.	Feb 2016	B1
9282032	Judge et al.	Mar 2016	B2
9294391	Mostert	Mar 2016	B1
9300535	Popli et al.	Mar 2016	B2
9323577	Marr et al.	Mar 2016	B2
9332078	Sivasubramanian et al.	Apr 2016	B2
9367929	Bettis et al.	May 2016	B2
9386038	Martini	Jun 2016	B2
9391949	Richardson et al.	Jul 2016	B1
9407676	Archer et al.	Jul 2016	B2
9407539	Dickinson et al.	Aug 2016	B1
9407681	Richardson et al.	Aug 2016	B1
9407699	Sivasubramanian et al.	Aug 2016	B2
9444718	Khakpour et al.	Aug 2016	B2
9444759	Richardson et al.	Sep 2016	B2
9479476	Richardson et al.	Sep 2016	B2
9491073	Baumback et al.	Oct 2016	B2
9495338	Hollis et al.	Nov 2016	B1
9497259	Richardson et al.	Nov 2016	B1
9515949	Richardson et al.	Nov 2016	B2
9525659	Sonkin	Dec 2016	B1
9544388	Li et al.	Jan 2017	B1
9544394	Richardson et al.	Jan 2017	B2
9571389	Richardson et al.	Jan 2017	B2
9575808	Yamala	Feb 2017	B1
9584328	Graham-cumming	Feb 2017	B1
9590946	Richardson et al.	Feb 2017	B2
9608957	Sivasubramanian et al.	Mar 2017	B2
9621660	Sivasubramanian et al.	Mar 2017	B2
9628403	Baumback et al.	Apr 2017	B2
9628509	Holloway et al.	Apr 2017	B2
9628554	Marshall et al.	Apr 2017	B2
9645808	Turpie	Apr 2017	B1
9660890	Baumback et al.	May 2017	B2
9703713	Nadgowda	May 2017	B2
9699108	Popli et al.	Jul 2017	B2
9705922	Foxhoven et al.	Jul 2017	B2
9712325	Richardson et al.	Jul 2017	B2
9712484	Richardson et al.	Jul 2017	B1
9734472	Richardson et al.	Jul 2017	B2
9742795	Radlein et al.	Aug 2017	B1
9760420	Letz et al.	Aug 2017	B1
9769248	Krishnan et al.	Sep 2017	B1
9774619	Radlein et al.	Sep 2017	B1
9787599	Richardson et al.	Sep 2017	B2
9787775	Richardson et al.	Oct 2017	B1
9794188	Baumback et al.	Oct 2017	B2
9794216	Richardson et al.	Oct 2017	B2
9794281	Radlein et al.	Oct 2017	B1
9800539	Richardson et al.	Oct 2017	B2
9811451	Arguelles et al.	Oct 2017	B1
9819567	Uppal et al.	Nov 2017	B1
9825831	Baumback et al.	Nov 2017	B2
9832141	Raftery	Nov 2017	B1
9871794	Joffe et al.	Jan 2018	B2
9887914	Bergman	Jan 2018	B2
9887915	Richardson et al.	Feb 2018	B2
9887931	Uppal et al.	Feb 2018	B1
9887932	Uppal et al.	Feb 2018	B1
9888089	Sivasubramanian et al.	Feb 2018	B2
9893957	Ellsworth et al.	Feb 2018	B2
9894168	Sivasubramanian et al.	Feb 2018	B2
9900402	Li et al.	Feb 2018	B1
9912740	Richardson et al.	Feb 2018	B2
9929959	Mostert	Mar 2018	B2
9930131	MacCarthaigh et al.	Mar 2018	B2
9954934	Sivasubramanian et al.	Mar 2018	B2
9985927	Richardson et al.	Apr 2018	B2
9992086	Mizik et al.	May 2018	B1
9992303	Richardson et al.	Jun 2018	B2
9996501	Nelson et al.	Jun 2018	B1
9996572	Calder et al.	Jun 2018	B2
10015237	Richardson et al.	Jun 2018	B2
10015241	Marr et al.	Jul 2018	B2
10027582	Richardson et al.	Jul 2018	B2
10027739	Krishnan et al.	Jul 2018	B1
10033627	Howard et al.	Jul 2018	B1
10033691	Mizik et al.	Jul 2018	B1
10033699	Sullivan et al.	Jul 2018	B2
10049051	Baldwin	Jul 2018	B1
10162753	Marshall et al.	Jul 2018	B2
10048974	Wagner	Aug 2018	B1
10063459	Judge et al.	Aug 2018	B2
10075551	Baldwin et al.	Aug 2018	B1
10079742	Richardson et al.	Sep 2018	B1
10091096	Howard et al.	Sep 2018	B1
10097398	Richardson et al.	Oct 2018	B1
10097448	Howard et al.	Oct 2018	B1
10097566	Radlein et al.	Oct 2018	B1
10104009	Baumback et al.	Oct 2018	B2
10110694	Watson et al.	Oct 2018	B1
10116584	Richardson et al.	Oct 2018	B2
10135620	Richardson et al.	Oct 2018	B2
10148542	Baumback et al.	Nov 2018	B2
10021179	Velummylum et al.	Dec 2018	B1
10157135	Richardson et al.	Dec 2018	B2
10158729	Sivasubramanian et al.	Dec 2018	B2
10180993	Raftery	Jan 2019	B2
10200402	Radlein et al.	Jan 2019	B2
10200492	MacCarthaigh et al.	Feb 2019	B2
10204041	Allen et al.	Feb 2019	B2
10205644	Baumback et al.	Feb 2019	B2
10205698	Petersen et al.	Feb 2019	B1
10218584	Ellsworth et al.	Feb 2019	B2
10225322	Richardson et al.	Feb 2019	B2
10225326	Puchala et al.	Mar 2019	B1
10225362	Watson	Mar 2019	B2
10225365	Hotchkies et al.	Mar 2019	B1
10230819	Richardson et al.	Mar 2019	B2
10257307	Baldwin	Mar 2019	B1
10264062	Richardson et al.	Apr 2019	B2
10270878	Uppal et al.	Apr 2019	B1
10284446	Baumback et al.	Apr 2019	B2
10305797	Richardson et al.	May 2019	B2
10311371	Hotchkies et al.	May 2019	B1
10348639	Puchala et al.	Jul 2019	B2
10372499	Radhakrishnan et al.	Aug 2019	B1
10374955	Mostert	Aug 2019	B2
10410085	Bettis et al.	Sep 2019	B2
10430084	Goss et al.	Oct 2019	B2
10447648	Bliss et al.	Oct 2019	B2
10462025	Baumback et al.	Oct 2019	B2
10467042	Mercier et al.	Nov 2019	B1
10469355	Uppal et al.	Nov 2019	B2
10469513	Uppal et al.	Nov 2019	B2
10491534	Richardson et al.	Nov 2019	B2
10505961	Uppal et al.	Dec 2019	B2
10506029	Hollis et al.	Dec 2019	B2
10511567	Richardson et al.	Dec 2019	B2
10516590	Mizik et al.	Dec 2019	B2
10521348	Marshall et al.	Dec 2019	B2
10523783	Richardson et al.	Dec 2019	B2
10530874	Sivasubramanian et al.	Jan 2020	B2
10542079	Marr et al.	Jan 2020	B2
10554748	Sivasubramanian et al.	Feb 2020	B2
10574787	Richardson et al.	Feb 2020	B2
10601767	Richardson et al.	Mar 2020	B2
10616250	Uppal et al.	Apr 2020	B2
10623408	Marshall et al.	Apr 2020	B1
10630771	Garza et al.	Apr 2020	B1
10645149	Sivasubramanian et al.	May 2020	B2
10666756	Baldwin et al.	May 2020	B2
10691752	Raftery	Jun 2020	B2
10742550	Richardson et al.	Aug 2020	B2
10742593	Vasquez et al.	Aug 2020	B1
10771552	Sivasubramanian et al.	Sep 2020	B2
10778554	Richardson et al.	Sep 2020	B2
10783077	Marshall et al.	Sep 2020	B2
10785037	Richardson et al.	Sep 2020	B2
10797995	Richardson et al.	Oct 2020	B2
10812358	Navaneetha et al.	Oct 2020	B2
10812846	Vantalon et al.	Oct 2020	B1
10931738	Radhakrishnan et al.	Feb 2021	B2
10938884	Baldwin et al.	Mar 2021	B1
10958501	Richardson et al.	Mar 2021	B1
11108729	Richardson et al.	Mar 2021	B2
11025747	Keogh	Jun 2021	B1
11115500	Richardson et al.	Sep 2021	B2
11134134	Uppal et al.	Sep 2021	B2
11194719	Richardson et al.	Dec 2021	B2
11205037	Hollis et al.	Dec 2021	B2
11245770	Sivasubramanian et al.	Feb 2022	B2
11283715	Richardson et al.	Mar 2022	B2
11290418	Vasquez et al.	Mar 2022	B2
11297140	Puchala et al.	Apr 2022	B2
11303717	Watson	Apr 2022	B2
11330008	Uppal et al.	May 2022	B2
11336712	Richardson et al.	May 2022	B2
11362986	Thunga et al.	Jun 2022	B2
11381487	Howard et al.	Jul 2022	B2
11451472	Richardson et al.	Sep 2022	B2
11457088	Watson et al.	Sep 2022	B2
20010000811	May et al.	May 2001	A1
20010025305	Yoshiasa et al.	Sep 2001	A1
20010027479	Delaney et al.	Oct 2001	A1
20010032133	Moran	Oct 2001	A1
20010034704	Farhat et al.	Oct 2001	A1
20010049741	Skene et al.	Dec 2001	A1
20010052016	Skene et al.	Dec 2001	A1
20010056416	Garcia-Luna-Aceves	Dec 2001	A1
20010056500	Farber et al.	Dec 2001	A1
20020002613	Freeman et al.	Jan 2002	A1
20020004816	Vange et al.	Jan 2002	A1
20020004846	Garcia-Luna-Aceves et al.	Jan 2002	A1
20020007404	Vange et al.	Jan 2002	A1
20020007413	Garcia-Luna-Aceves et al.	Jan 2002	A1
20020009079	Jungck et al.	Jan 2002	A1
20020010783	Primak et al.	Jan 2002	A1
20020010798	Ben-Shaul et al.	Jan 2002	A1
20020013823	Eubanks	Jan 2002	A1
20020016831	Peled et al.	Feb 2002	A1
20020035624	Kim	Mar 2002	A1
20020048269	Hong et al.	Apr 2002	A1
20020049608	Hartsell et al.	Apr 2002	A1
20020049842	Huetsch et al.	Apr 2002	A1
20020049857	Farber et al.	Apr 2002	A1
20020052942	Swildens et al.	May 2002	A1
20020062372	Hong et al.	May 2002	A1
20020065910	Dutta	May 2002	A1
20020068554	Dusse	Jun 2002	A1
20020069420	Russell et al.	Jun 2002	A1
20020078233	Biliris et al.	Jun 2002	A1
20020082858	Heddaya et al.	Jun 2002	A1
20020083118	Sim	Jun 2002	A1
20020083148	Shaw et al.	Jun 2002	A1
20020083175	Afek et al.	Jun 2002	A1
20020083178	Brothers	Jun 2002	A1
20020083198	Kim et al.	Jun 2002	A1
20020087374	Boubez et al.	Jul 2002	A1
20020087726	Macpherson et al.	Jul 2002	A1
20020087797	Adrangi	Jul 2002	A1
20020091786	Yamaguchi et al.	Jul 2002	A1
20020091801	Lewin et al.	Jul 2002	A1
20020092026	Janniello et al.	Jul 2002	A1
20020099616	Sweldens	Jul 2002	A1
20020099850	Farber et al.	Jul 2002	A1
20020101836	Dorenbosch	Aug 2002	A1
20020103820	Cartmell et al.	Aug 2002	A1
20020103972	Satran et al.	Aug 2002	A1
20020107944	Bai et al.	Aug 2002	A1
20020112049	Elnozahy et al.	Aug 2002	A1
20020112123	Becker et al.	Aug 2002	A1
20020116481	Lee	Aug 2002	A1
20020116491	Boyd et al.	Aug 2002	A1
20020116582	Copeland et al.	Aug 2002	A1
20020120666	Landsman et al.	Aug 2002	A1
20020120782	Dillon et al.	Aug 2002	A1
20020124047	Gartner et al.	Sep 2002	A1
20020124098	Shaw	Sep 2002	A1
20020129123	Johnson et al.	Sep 2002	A1
20020131428	Pecus et al.	Sep 2002	A1
20020133601	Kennamer et al.	Sep 2002	A1
20020133741	Maeda et al.	Sep 2002	A1
20020135611	Deosaran et al.	Sep 2002	A1
20020138286	Engstrom	Sep 2002	A1
20020138437	Lewin et al.	Sep 2002	A1
20020138443	Schran et al.	Sep 2002	A1
20020138649	Cartmell et al.	Sep 2002	A1
20020138761	Kanemaki et al.	Sep 2002	A1
20020143675	Orshan	Oct 2002	A1
20020143798	Lisiecki et al.	Oct 2002	A1
20020143989	Huitema et al.	Oct 2002	A1
20020145993	Chowdhury et al.	Oct 2002	A1
20020147770	Tang	Oct 2002	A1
20020147774	Lisiecki et al.	Oct 2002	A1
20020150094	Cheng et al.	Oct 2002	A1
20020150276	Chang	Oct 2002	A1
20020152326	Orshan	Oct 2002	A1
20020154157	Sherr et al.	Oct 2002	A1
20020156884	Bertram et al.	Oct 2002	A1
20020156911	Croman et al.	Oct 2002	A1
20020161745	Call	Oct 2002	A1
20020161767	Shapiro et al.	Oct 2002	A1
20020163882	Bornstein et al.	Nov 2002	A1
20020165912	Wenocur et al.	Nov 2002	A1
20020169890	Beaumont et al.	Nov 2002	A1
20020184368	Wang	Dec 2002	A1
20020187935	Redmond et al.	Dec 2002	A1
20020188722	Banerjee et al.	Dec 2002	A1
20020194324	Guha	Dec 2002	A1
20020194382	Kausik et al.	Dec 2002	A1
20020198953	O'Rourke et al.	Dec 2002	A1
20030002484	Freedman	Jan 2003	A1
20030004998	Datta	Jan 2003	A1
20030005036	Mitzenmacher	Jan 2003	A1
20030005111	Allan	Jan 2003	A1
20030007482	Khello et al.	Jan 2003	A1
20030009488	Hart, III	Jan 2003	A1
20030009562	Heymann et al.	Jan 2003	A1
20030009591	Hayball et al.	Jan 2003	A1
20030002641	Lumsden	Feb 2003	A1
20030028642	Agarwal et al.	Feb 2003	A1
20030033283	Evans et al.	Feb 2003	A1
20030037108	Peiffer et al.	Feb 2003	A1
20030037139	Shteyn	Feb 2003	A1
20030037284	Srinivasan et al.	Feb 2003	A1
20030041094	Lara et al.	Feb 2003	A1
20030046343	Krishnamurthy et al.	Mar 2003	A1
20030065739	Shnier	Apr 2003	A1
20030070096	Pazi et al.	Apr 2003	A1
20030074401	Connell et al.	Apr 2003	A1
20030074471	Anderson et al.	Apr 2003	A1
20030074472	Lucco et al.	Apr 2003	A1
20030079027	Slocombe et al.	Apr 2003	A1
20030093523	Cranor et al.	May 2003	A1
20030093691	Simon et al.	May 2003	A1
20030097564	Tewari et al.	May 2003	A1
20030099202	Lear et al.	May 2003	A1
20030099237	Mitra et al.	May 2003	A1
20030101278	Garcia-Luna-Aceves et al.	May 2003	A1
20030105829	Hayward	Jun 2003	A1
20030105857	Kamen et al.	Jun 2003	A1
20030112792	Cranor et al.	Jun 2003	A1
20030120741	Wu et al.	Jun 2003	A1
20030126387	Watanabe	Jul 2003	A1
20030133554	Nykanen et al.	Jul 2003	A1
20030135467	Okamoto	Jul 2003	A1
20030135468	Barbir et al.	Jul 2003	A1
20030135509	Davis et al.	Jul 2003	A1
20030140087	Lincoln et al.	Jul 2003	A1
20030145038	Bin Tariq et al.	Jul 2003	A1
20030145066	Okada et al.	Jul 2003	A1
20030149581	Chaudhri et al.	Aug 2003	A1
20030154239	Davis et al.	Aug 2003	A1
20030154284	Bernardin et al.	Aug 2003	A1
20030163722	Anderson, IV	Aug 2003	A1
20030172145	Nguyen	Sep 2003	A1
20030172183	Anderson, IV et al.	Sep 2003	A1
20030172291	Judge et al.	Sep 2003	A1
20030174648	Wang et al.	Sep 2003	A1
20030177321	Watanabe	Sep 2003	A1
20030182305	Balva et al.	Sep 2003	A1
20030182413	Allen et al.	Sep 2003	A1
20030182447	Schilling	Sep 2003	A1
20030187935	Agarwalla et al.	Oct 2003	A1
20030187970	Chase et al.	Oct 2003	A1
20030191822	Leighton et al.	Oct 2003	A1
20030200394	Ashmore et al.	Oct 2003	A1
20030204602	Hudson et al.	Oct 2003	A1
20030204742	Gupta et al.	Oct 2003	A1
20030206520	Wu et al.	Nov 2003	A1
20030221000	Cherkasova et al.	Nov 2003	A1
20030225893	Roese et al.	Dec 2003	A1
20030229682	Day	Dec 2003	A1
20030233423	Dilley et al.	Dec 2003	A1
20030233445	Levy et al.	Dec 2003	A1
20030233455	Leber et al.	Dec 2003	A1
20030236700	Arning et al.	Dec 2003	A1
20030236779	Choi et al.	Dec 2003	A1
20040003032	Ma et al.	Jan 2004	A1
20040010562	Itonaga	Jan 2004	A1
20040010563	Forte et al.	Jan 2004	A1
20040010588	Slater et al.	Jan 2004	A1
20040010601	Afergan et al.	Jan 2004	A1
20040010621	Afergan et al.	Jan 2004	A1
20040010683	Huitema	Jan 2004	A1
20040015584	Cartmell et al.	Jan 2004	A1
20040019518	Abraham et al.	Jan 2004	A1
20040019781	Chari et al.	Jan 2004	A1
20040024841	Becker et al.	Jan 2004	A1
20040030620	Benjamin et al.	Feb 2004	A1
20040032278	Orii et al.	Feb 2004	A1
20040034744	Karlsson et al.	Feb 2004	A1
20040039798	Hotz et al.	Feb 2004	A1
20040044731	Chen et al.	Feb 2004	A1
20040044791	Pouzzner	Mar 2004	A1
20040049579	Ims et al.	Mar 2004	A1
20040054757	Ueda et al.	Mar 2004	A1
20040059805	Dinker et al.	Mar 2004	A1
20040064335	Yang	Apr 2004	A1
20040064501	Jan et al.	Apr 2004	A1
20040068542	Lalonde et al.	Apr 2004	A1
20040073596	Kloninger et al.	Apr 2004	A1
20040073707	Dillon	Apr 2004	A1
20040073867	Kausik et al.	Apr 2004	A1
20040078468	Hedin et al.	Apr 2004	A1
20040078487	Cernohous et al.	Apr 2004	A1
20040083283	Sundaram et al.	Apr 2004	A1
20040083307	Uysal	Apr 2004	A1
20040105544	Haneda et al.	Apr 2004	A1
20040098478	Koetke et al.	May 2004	A1
20040114579	Karaoguz et al.	Jun 2004	A1
20040117309	Inoue et al.	Jun 2004	A1
20040117455	Kaminksy et al.	Jun 2004	A1
20040128344	Trossen	Jun 2004	A1
20040128346	Melamed et al.	Jul 2004	A1
20040148520	Talpade et al.	Jul 2004	A1
20040167981	Douglas et al.	Jul 2004	A1
20040167982	Cohen et al.	Aug 2004	A1
20040170379	Yao et al.	Aug 2004	A1
20040172466	Douglas et al.	Sep 2004	A1
20040184456	Binding et al.	Sep 2004	A1
20040194085	Beaubien et al.	Sep 2004	A1
20040194102	Neerdaels	Sep 2004	A1
20040203630	Wang	Oct 2004	A1
20040205149	Dillon et al.	Oct 2004	A1
20040205162	Parikh	Oct 2004	A1
20040205374	Poletto et al.	Oct 2004	A1
20040215823	Kleinfelter et al.	Oct 2004	A1
20040221019	Swildens et al.	Oct 2004	A1
20040221034	Kausik et al.	Nov 2004	A1
20040246948	Lee et al.	Nov 2004	A1
20040249939	Amini et al.	Dec 2004	A1
20040249971	Klinker	Dec 2004	A1
20040249975	Tuck et al.	Dec 2004	A1
20040250119	Shelest et al.	Dec 2004	A1
20040254921	Cohen et al.	Dec 2004	A1
20040260769	Yamamoto	Dec 2004	A1
20040267906	Truty	Dec 2004	A1
20040267907	Gustafsson	Dec 2004	A1
20050004945	Cossins et al.	Jan 2005	A1
20050010653	McCanne	Jan 2005	A1
20050010961	Hagen	Jan 2005	A1
20050015471	Zhang et al.	Jan 2005	A1
20050021706	Maggi et al.	Jan 2005	A1
20050021862	Schroeder et al.	Jan 2005	A1
20050027882	Sullivan et al.	Jan 2005	A1
20050038967	Umbehocker et al.	Feb 2005	A1
20050039019	Delany	Feb 2005	A1
20050044270	Grove et al.	Feb 2005	A1
20050076137	Tang et al.	Apr 2005	A1
20050102683	Branson et al.	Apr 2005	A1
20050097445	Day et al.	May 2005	A1
20050108169	Balasubramanian et al.	May 2005	A1
20050108262	Fawcett	May 2005	A1
20050108529	Juneau	May 2005	A1
20050114296	Farber et al.	May 2005	A1
20050117717	Lumsden	May 2005	A1
20050132083	Raciborski et al.	Jun 2005	A1
20050147088	Bao et al.	Jun 2005	A1
20050149529	Gutmans	Jul 2005	A1
20050157712	Rangarajan et al.	Jul 2005	A1
20050160133	Greenlee et al.	Jul 2005	A1
20050163168	Sheth et al.	Jul 2005	A1
20050168782	Kobashi et al.	Jul 2005	A1
20050171959	Deforche et al.	Aug 2005	A1
20050172080	Miyauchi	Aug 2005	A1
20050174989	Chen et al.	Aug 2005	A1
20050181769	Kogawa	Aug 2005	A1
20050188073	Nakamichi et al.	Aug 2005	A1
20050192814	Challener et al.	Aug 2005	A1
20050192008	Desai et al.	Sep 2005	A1
20050198170	LeMay et al.	Sep 2005	A1
20050198200	Subramanian et al.	Sep 2005	A1
20050198303	Knauerhase et al.	Sep 2005	A1
20050198334	Farber et al.	Sep 2005	A1
20050198453	Osaki	Sep 2005	A1
20050198571	Kramer et al.	Sep 2005	A1
20050201302	Gaddis et al.	Sep 2005	A1
20050216483	Armstrong et al.	Sep 2005	A1
20050216569	Coppola et al.	Sep 2005	A1
20050216674	Robbin et al.	Sep 2005	A1
20050223095	Volz et al.	Oct 2005	A1
20050228856	Swildens et al.	Oct 2005	A1
20050229119	Torvinen	Oct 2005	A1
20050232165	Brawn et al.	Oct 2005	A1
20050234864	Shapiro	Oct 2005	A1
20050240574	Challenger et al.	Oct 2005	A1
20050256880	Nam Koong et al.	Oct 2005	A1
20050259645	Chen et al.	Nov 2005	A1
20050259672	Eduri	Nov 2005	A1
20050262248	Jennings, III et al.	Nov 2005	A1
20050266835	Agrawal et al.	Nov 2005	A1
20050267928	Anderson et al.	Dec 2005	A1
20050267937	Daniels et al.	Dec 2005	A1
20050267991	Huitema et al.	Dec 2005	A1
20050267992	Huitema et al.	Dec 2005	A1
20050267993	Huitema et al.	Dec 2005	A1
20050278259	Gunaseelan et al.	Dec 2005	A1
20050283759	Peteanu et al.	Dec 2005	A1
20050283784	Suzuki	Dec 2005	A1
20050286564	Hatley et al.	Dec 2005	A1
20060005014	Aura et al.	Jan 2006	A1
20060013158	Ahuja et al.	Jan 2006	A1
20060020596	Liu et al.	Jan 2006	A1
20060020684	Mukherjee et al.	Jan 2006	A1
20060020714	Girouard et al.	Jan 2006	A1
20060020715	Jungck	Jan 2006	A1
20060020807	Aura et al.	Jan 2006	A1
20060021001	Giles et al.	Jan 2006	A1
20060026067	Nicholas et al.	Jan 2006	A1
20060026154	Altinel et al.	Feb 2006	A1
20060026592	Simonen	Feb 2006	A1
20060031239	Koenig	Feb 2006	A1
20060031319	Nelson et al.	Feb 2006	A1
20060031503	Gilbert	Feb 2006	A1
20060034494	Holloran	Feb 2006	A1
20060036720	Faulk, Jr.	Feb 2006	A1
20060036966	Yevdayev	Feb 2006	A1
20060037037	Miranz	Feb 2006	A1
20060039352	Karstens	Feb 2006	A1
20060041614	Oe	Feb 2006	A1
20060045005	Blackmore et al.	Feb 2006	A1
20060047787	Aggarwal et al.	Mar 2006	A1
20060047813	Aggarwal et al.	Mar 2006	A1
20060059246	Grove	Mar 2006	A1
20060063534	Kokkonen et al.	Mar 2006	A1
20060064476	Decasper et al.	Mar 2006	A1
20060064500	Roth et al.	Mar 2006	A1
20060074750	Clark et al.	Mar 2006	A1
20060075084	Lyon	Apr 2006	A1
20060075139	Jungck	Apr 2006	A1
20060083165	McLane et al.	Apr 2006	A1
20060085536	Meyer et al.	Apr 2006	A1
20060088026	Mazur et al.	Apr 2006	A1
20060106938	Dini et al.	Apr 2006	A1
20060107036	Randle et al.	May 2006	A1
20060112066	Hamzy	May 2006	A1
20060112176	Liu et al.	May 2006	A1
20060120385	Atchison et al.	May 2006	A1
20060129665	Toebes et al.	Jun 2006	A1
20060129766	Cassia et al.	Jun 2006	A1
20060136453	Kwan	Jun 2006	A1
20060143293	Freedman	Jun 2006	A1
20060143442	Smith	Jun 2006	A1
20060146820	Friedman et al.	Jun 2006	A1
20060149529	Nguyen et al.	Jul 2006	A1
20060155823	Tran et al.	Jul 2006	A1
20060155862	Kathi et al.	Jul 2006	A1
20060161541	Cencini	Jul 2006	A1
20060165051	Banerjee et al.	Jul 2006	A1
20060168088	Leighton et al.	Jul 2006	A1
20060168240	Olshefski	Jul 2006	A1
20060173957	Robinson	Jul 2006	A1
20060173855	Turner et al.	Aug 2006	A1
20060179080	Meek et al.	Aug 2006	A1
20060184936	Abels et al.	Aug 2006	A1
20060188097	Taniguchi et al.	Aug 2006	A1
20060190605	Franz et al.	Aug 2006	A1
20060193247	Naseh et al.	Aug 2006	A1
20060195866	Thukral	Aug 2006	A1
20060206568	Verma et al.	Aug 2006	A1
20060206586	Ling et al.	Sep 2006	A1
20060218265	Farber et al.	Sep 2006	A1
20060218304	Mukherjee et al.	Sep 2006	A1
20060221971	Andrieux et al.	Sep 2006	A1
20060224752	Parekh et al.	Oct 2006	A1
20060227740	McLaughlin et al.	Oct 2006	A1
20060227758	Rana et al.	Oct 2006	A1
20060230137	Gare et al.	Oct 2006	A1
20060230265	Krishna	Oct 2006	A1
20060233155	Srivastava	Oct 2006	A1
20060235941	Areas et al.	Oct 2006	A1
20060242227	Rao	Oct 2006	A1
20060253546	Chang et al.	Nov 2006	A1
20060253609	Andreev et al.	Nov 2006	A1
20060259581	Piersol	Nov 2006	A1
20060259690	Vittal et al.	Nov 2006	A1
20060259984	Juneau	Nov 2006	A1
20060265497	Ohata et al.	Nov 2006	A1
20060265508	Angel et al.	Nov 2006	A1
20060265516	Schilling	Nov 2006	A1
20060265720	Cai et al.	Nov 2006	A1
20060271641	Stavrakos et al.	Nov 2006	A1
20060282522	Lewin et al.	Nov 2006	A1
20060070060	Tantawi et al.	Dec 2006	A1
20060282505	Hasha et al.	Dec 2006	A1
20060288119	Kim et al.	Dec 2006	A1
20060288424	Saito	Dec 2006	A1
20070005689	Leighton et al.	Jan 2007	A1
20070005801	Kumar et al.	Jan 2007	A1
20070005892	Mullender et al.	Jan 2007	A1
20070011267	Overton et al.	Jan 2007	A1
20070014241	Banerjee et al.	Jan 2007	A1
20070021998	Laithwaite et al.	Jan 2007	A1
20070028001	Phillips et al.	Jan 2007	A1
20070038729	Sullivan et al.	Feb 2007	A1
20070038994	Davis et al.	Feb 2007	A1
20070041393	Westhead et al.	Feb 2007	A1
20070043667	Qawami et al.	Feb 2007	A1
20070043859	Ruul	Feb 2007	A1
20070050522	Grove et al.	Feb 2007	A1
20070050703	Lebel	Mar 2007	A1
20070055764	Dilley et al.	Mar 2007	A1
20070055765	Lisiecki et al.	Mar 2007	A1
20070061440	Sundaram et al.	Mar 2007	A1
20070064610	Khandani	Mar 2007	A1
20070076872	Juneau	Mar 2007	A1
20070086429	Lawrence et al.	Apr 2007	A1
20070094361	Nowski et al.	Apr 2007	A1
20070101377	Six et al.	Apr 2007	A1
20070101061	Baskaran et al.	May 2007	A1
20070118667	McCarthy et al.	May 2007	A1
20070118668	McCarthy et al.	May 2007	A1
20070124309	Takase et al.	May 2007	A1
20070134641	Lieu	May 2007	A1
20070156726	Levy	Jun 2007	A1
20070156919	Potti et al.	Jul 2007	A1
20070162331	Sullivan	Jul 2007	A1
20070168336	Ransil et al.	Jul 2007	A1
20070168517	Weller	Jul 2007	A1
20070174426	Swildens et al.	Jul 2007	A1
20070174442	Sherman et al.	Jul 2007	A1
20070174490	Choi et al.	Jul 2007	A1
20070183342	Wong et al.	Jul 2007	A1
20070195800	Yang et al.	Aug 2007	A1
20070198982	Bolan et al.	Aug 2007	A1
20070204107	Greenfield et al.	Aug 2007	A1
20070208737	Li et al.	Aug 2007	A1
20070214232	Belimpasakis et al.	Sep 2007	A1
20070219795	Park et al.	Sep 2007	A1
20070220010	Ertugrul	Sep 2007	A1
20070226294	Pruitt et al.	Sep 2007	A1
20070233705	Farber et al.	Sep 2007	A1
20070233706	Farber et al.	Oct 2007	A1
20070233846	Farber et al.	Oct 2007	A1
20070233884	Farber et al.	Oct 2007	A1
20070233896	Hilt et al.	Oct 2007	A1
20070242824	Vishik	Oct 2007	A1
20070243860	Aiello et al.	Oct 2007	A1
20070244964	Challenger et al.	Oct 2007	A1
20070245022	Olliphant et al.	Oct 2007	A1
20070250467	Mesnik et al.	Oct 2007	A1
20070250468	Pieper	Oct 2007	A1
20070250560	Wein et al.	Oct 2007	A1
20070250601	Amlekar et al.	Oct 2007	A1
20070250611	Bhogal et al.	Oct 2007	A1
20070253377	Janneteau et al.	Oct 2007	A1
20070255843	Zubev	Nov 2007	A1
20070263604	Tai	Nov 2007	A1
20070266113	Koopmans et al.	Nov 2007	A1
20070266311	Westphal	Nov 2007	A1
20070266333	Cossey et al.	Nov 2007	A1
20070270165	Poosala	Nov 2007	A1
20070271375	Hwang	Nov 2007	A1
20070271385	Davis et al.	Nov 2007	A1
20070271560	Wahlert et al.	Nov 2007	A1
20070271608	Shimizu et al.	Nov 2007	A1
20070280197	Pearlman et al.	Nov 2007	A1
20070280229	Kenney	Dec 2007	A1
20070281689	Altman et al.	Dec 2007	A1
20070288588	Wein et al.	Dec 2007	A1
20070291739	Sullivan et al.	Dec 2007	A1
20070294419	Ulevitch	Dec 2007	A1
20080005057	Ozzie et al.	Jan 2008	A1
20080005275	Overton et al.	Jan 2008	A1
20080008089	Bornstein et al.	Jan 2008	A1
20080016233	Schneider	Jan 2008	A1
20080028463	Dagon et al.	Jan 2008	A1
20080222647	Taylor et al.	Jan 2008	A1
20080037536	Padmanabhan et al.	Feb 2008	A1
20080046550	Mazur et al.	Feb 2008	A1
20080046596	Afergan et al.	Feb 2008	A1
20080049615	Bugenhagen	Feb 2008	A1
20080056207	Eriksson et al.	Feb 2008	A1
20080062997	Nix	Mar 2008	A1
20080065724	Seed et al.	Mar 2008	A1
20080065745	Leighton et al.	Mar 2008	A1
20080066072	Yurekli et al.	Mar 2008	A1
20080071859	Seed et al.	Mar 2008	A1
20080071925	Leighton et al.	Mar 2008	A1
20080071987	Karn et al.	Mar 2008	A1
20080072264	Crayford	Mar 2008	A1
20080082551	Farber et al.	Mar 2008	A1
20080082662	Dandliker et al.	Apr 2008	A1
20080086434	Chesla	Apr 2008	A1
20080086559	Davis et al.	Apr 2008	A1
20080086574	Raciborski et al.	Apr 2008	A1
20080092242	Rowley	Apr 2008	A1
20080101358	Van Ewijk et al.	Apr 2008	A1
20080103805	Shear et al.	May 2008	A1
20080104268	Farber et al.	May 2008	A1
20080109679	Wright et al.	May 2008	A1
20080114829	Button et al.	May 2008	A1
20080125077	Velazquez et al.	May 2008	A1
20080126706	Newport et al.	May 2008	A1
20080134043	Georgis et al.	May 2008	A1
20080140800	Farber et al.	Jun 2008	A1
20080147866	Stolorz et al.	Jun 2008	A1
20080147873	Matsumoto	Jun 2008	A1
20080155059	Hardin et al.	Jun 2008	A1
20080155061	Afergan et al.	Jun 2008	A1
20080155613	Benya et al.	Jun 2008	A1
20080155614	Cooper et al.	Jun 2008	A1
20080155694	Kwon et al.	Jun 2008	A1
20080162667	Verma et al.	Jun 2008	A1
20080162821	Duran et al.	Jul 2008	A1
20080162843	Davis et al.	Jul 2008	A1
20080172488	Jawahar et al.	Jul 2008	A1
20080175222	Barnea et al.	Jul 2008	A1
20080184357	Drako et al.	Jul 2008	A1
20080189437	Halley	Jul 2008	A1
20080201332	Souders et al.	Aug 2008	A1
20080201401	Pugh et al.	Aug 2008	A1
20080201711	Amir Husain	Aug 2008	A1
20080025304	Venkataswami et al.	Sep 2008	A1
20080215718	Stolorz et al.	Sep 2008	A1
20080215730	Sundaram et al.	Sep 2008	A1
20080215735	Farber et al.	Sep 2008	A1
20080215747	Menon et al.	Sep 2008	A1
20080215750	Farber et al.	Sep 2008	A1
20080215755	Farber et al.	Sep 2008	A1
20080222281	Dilley et al.	Sep 2008	A1
20080222291	Weller et al.	Sep 2008	A1
20080222295	Robinson et al.	Sep 2008	A1
20080225779	Bragiel et al.	Sep 2008	A1
20080228574	Stewart et al.	Sep 2008	A1
20080228920	Souders et al.	Sep 2008	A1
20080235383	Schneider	Sep 2008	A1
20080235400	Slocombe et al.	Sep 2008	A1
20080256087	Piironen et al.	Oct 2008	A1
20080256175	Lee et al.	Oct 2008	A1
20080263135	Olliphant	Oct 2008	A1
20080270882	Rollins et al.	Oct 2008	A1
20080275772	Suryanarayana et al.	Oct 2008	A1
20080281946	Swildens et al.	Nov 2008	A1
20080281950	Wald et al.	Nov 2008	A1
20080288458	Sun et al.	Nov 2008	A1
20080288722	Lecoq et al.	Nov 2008	A1
20080301670	Gouge et al.	Nov 2008	A1
20080312766	Couckuyt	Dec 2008	A1
20080319862	Golan et al.	Dec 2008	A1
20080320123	Houlihan et al.	Dec 2008	A1
20080320269	Houlihan et al.	Dec 2008	A1
20090013063	Soman	Jan 2009	A1
20090013321	Mattiocco	Jan 2009	A1
20090016236	Alcala et al.	Jan 2009	A1
20090029644	Sue et al.	Jan 2009	A1
20090031042	Phatak	Jan 2009	A1
20090031367	Sue	Jan 2009	A1
20090031368	Ling	Jan 2009	A1
20090031376	Riley et al.	Jan 2009	A1
20090043900	Barber	Feb 2009	A1
20090049098	Pickelsimer et al.	Feb 2009	A1
20090063038	Shrivathsan et al.	Feb 2009	A1
20090063704	Taylor et al.	Mar 2009	A1
20090070533	Elazary et al.	Mar 2009	A1
20090083228	Shatz et al.	Mar 2009	A1
20090083279	Hasek	Mar 2009	A1
20090083413	Levow et al.	Mar 2009	A1
20090086728	Gulati et al.	Mar 2009	A1
20090086741	Zhang	Apr 2009	A1
20090089869	Varghese	Apr 2009	A1
20090094252	Wong et al.	Apr 2009	A1
20090103707	McGary et al.	Apr 2009	A1
20090106202	Mizrahi	Apr 2009	A1
20090106381	Kasriel et al.	Apr 2009	A1
20090109854	Rajpathak	Apr 2009	A1
20090112703	Brown	Apr 2009	A1
20090125393	Hwang et al.	May 2009	A1
20090125902	Ghosh	May 2009	A1
20090125934	Jones et al.	May 2009	A1
20090132368	Cotter et al.	May 2009	A1
20090132640	Verma et al.	May 2009	A1
20090132648	Swildens et al.	May 2009	A1
20090138533	Iwasaki et al.	May 2009	A1
20090138582	Turk	May 2009	A1
20090144411	Winkler et al.	May 2009	A1
20090144412	Ferguson et al.	Jun 2009	A1
20090144496	Kawaguchi	Jun 2009	A1
20090150926	Schlack	Jun 2009	A1
20090157504	Braemer et al.	Jun 2009	A1
20090157850	Gagliardi et al.	Jun 2009	A1
20090157899	Gagliardi et al.	Jun 2009	A1
20090158163	Stephens et al.	Jun 2009	A1
20090164331	Bishop et al.	Jun 2009	A1
20090164614	Christian et al.	Jun 2009	A1
20090177667	Ramos et al.	Jun 2009	A1
20090172167	Drai et al.	Jul 2009	A1
20090182815	Czechowski et al.	Jul 2009	A1
20090182837	Rogers	Jul 2009	A1
20090182945	Aviles et al.	Jul 2009	A1
20090187575	DaCosta	Jul 2009	A1
20090198817	Sundaram et al.	Jul 2009	A1
20090204682	Jeyaseelan et al.	Aug 2009	A1
20090210549	Hudson et al.	Aug 2009	A1
20090228708	Trostle	Aug 2009	A1
20090233623	Johnson	Sep 2009	A1
20090241167	Moore	Sep 2009	A1
20090248697	Richardson et al.	Sep 2009	A1
20090248786	Richardson et al.	Oct 2009	A1
20090248787	Sivasubramanian et al.	Oct 2009	A1
20090248852	Fuhrmann et al.	Oct 2009	A1
20090248858	Sivasubramanian et al.	Oct 2009	A1
20090248893	Richardson et al.	Oct 2009	A1
20090249222	Schmidt et al.	Oct 2009	A1
20090253435	Olofsson	Oct 2009	A1
20090254661	Fullagar et al.	Oct 2009	A1
20090254989	Achan et al.	Oct 2009	A1
20090259588	Lindsay	Oct 2009	A1
20090259971	Rankine et al.	Oct 2009	A1
20090262741	Jungck et al.	Oct 2009	A1
20090265707	Goodman et al.	Oct 2009	A1
20090265786	Xie et al.	Oct 2009	A1
20090271498	Cable	Oct 2009	A1
20090271577	Campana et al.	Oct 2009	A1
20090271730	Rose et al.	Oct 2009	A1
20090276771	Nickolov et al.	Oct 2009	A1
20090279444	Ravindran et al.	Nov 2009	A1
20090282038	Subotin et al.	Nov 2009	A1
20090287750	Banavar et al.	Nov 2009	A1
20090307307	Igarashi	Nov 2009	A1
20090327489	Swildens et al.	Dec 2009	A1
20090327517	Sivasubramanian et al.	Dec 2009	A1
20090327914	Adar et al.	Dec 2009	A1
20100005175	Swildens et al.	Jan 2010	A1
20100011061	Hudson et al.	Jan 2010	A1
20100011126	Hsu et al.	Jan 2010	A1
20100020699	On	Jan 2010	A1
20100023601	Lewin et al.	Jan 2010	A1
20100023621	Ezolt et al.	Jan 2010	A1
20100030662	Klein	Jan 2010	A1
20100030914	Sparks et al.	Feb 2010	A1
20100034381	Trace et al.	Feb 2010	A1
20100034470	Valencia-Campo et al.	Feb 2010	A1
20100036944	Douglis et al.	Feb 2010	A1
20100037314	Perdisci et al.	Feb 2010	A1
20100042725	Jeon et al.	Feb 2010	A1
20100049862	Dixon	Feb 2010	A1
20100057894	Glasser	Feb 2010	A1
20100058352	Esfahany	Mar 2010	A1
20100070603	Moss et al.	Mar 2010	A1
20100070700	Borst et al.	Mar 2010	A1
20100074268	Raza	Mar 2010	A1
20100082320	Wood et al.	Mar 2010	A1
20100082787	Kommula et al.	Apr 2010	A1
20100088367	Brown et al.	Apr 2010	A1
20100088405	Huang et al.	Apr 2010	A1
20100095008	Joshi	Apr 2010	A1
20100100629	Raciborski et al.	Apr 2010	A1
20100103837	Jungck et al.	Apr 2010	A1
20100111059	Bappu et al.	Apr 2010	A1
20100161564	Lee et al.	Apr 2010	A1
20100115133	Joshi	May 2010	A1
20100115342	Shigeta et al.	May 2010	A1
20100121953	Friedman et al.	May 2010	A1
20100121981	Drako	May 2010	A1
20100122069	Gonion	May 2010	A1
20100125626	Lucas et al.	May 2010	A1
20100125673	Richardson et al.	May 2010	A1
20100125675	Richardson et al.	May 2010	A1
20100131646	Drako	May 2010	A1
20100138559	Sullivan et al.	May 2010	A1
20100106934	Calder et al.	Jun 2010	A1
20100138921	Na	Jun 2010	A1
20100150155	Napierala	Jun 2010	A1
20100161565	Lee et al.	Jun 2010	A1
20100161799	Maloo	Jun 2010	A1
20100169392	Lev Ran et al.	Jun 2010	A1
20100169452	Atluri et al.	Jul 2010	A1
20100174811	Musiri et al.	Jul 2010	A1
20100191854	Isci et al.	Jul 2010	A1
20100192225	Ma et al.	Jul 2010	A1
20100217801	Leighton et al.	Aug 2010	A1
20100217856	Falkena	Aug 2010	A1
20100223364	Wei	Aug 2010	A1
20100025071	Cadwell et al.	Sep 2010	A1
20100226372	Watanabe	Sep 2010	A1
20100228819	Wei	Sep 2010	A1
20100235915	Memon et al.	Sep 2010	A1
20100257024	Holmes et al.	Sep 2010	A1
20100257266	Holmes et al.	Oct 2010	A1
20100257566	Matila	Oct 2010	A1
20100262964	Uyeda et al.	Oct 2010	A1
20100268789	Yoo et al.	Oct 2010	A1
20100268814	Cross et al.	Oct 2010	A1
20100274765	Murphy et al.	Oct 2010	A1
20100281482	Pike et al.	Oct 2010	A1
20100293296	Hsu et al.	Nov 2010	A1
20100293479	Rousso et al.	Nov 2010	A1
20100299427	Joshi	Nov 2010	A1
20100299438	Zimmerman et al.	Nov 2010	A1
20100299439	McCarthy et al.	Nov 2010	A1
20100306382	Cardosa et al.	Nov 2010	A1
20100312861	Kolhi et al.	Dec 2010	A1
20100318508	Brawer et al.	Dec 2010	A1
20100322255	Hao et al.	Dec 2010	A1
20100325365	Colglazier et al.	Dec 2010	A1
20100332595	Fullagar et al.	Dec 2010	A1
20100332601	Walter et al.	Dec 2010	A1
20100332658	Elyashev	Dec 2010	A1
20110010244	Hatridge	Jan 2011	A1
20110016214	Jackson	Jan 2011	A1
20110029598	Arnold et al.	Jan 2011	A1
20110029398	Boudville	Feb 2011	A1
20110035469	Smith et al.	Feb 2011	A1
20110040893	Karaoguz et al.	Feb 2011	A1
20110051738	Xu	Feb 2011	A1
20110055386	Middleton et al.	Mar 2011	A1
20110055714	Vemulapalli et al.	Mar 2011	A1
20110055921	Narayanaswamy et al.	Mar 2011	A1
20110057790	Martin et al.	Mar 2011	A1
20110058675	Brueck et al.	Mar 2011	A1
20110072138	Canturk et al.	Mar 2011	A1
20110072366	Spencer	Mar 2011	A1
20110078000	Ma et al.	Mar 2011	A1
20110078230	Sepulveda	Mar 2011	A1
20110085654	Jana et al.	Mar 2011	A1
20110082916	Swanson et al.	Apr 2011	A1
20110087769	Holmes et al.	Apr 2011	A1
20110093584	Qiu et al.	Apr 2011	A1
20110096987	Morales et al.	Apr 2011	A1
20110099294	Kapur et al.	Apr 2011	A1
20110106949	Patel et al.	Apr 2011	A1
20110113467	Agarwal et al.	May 2011	A1
20110125894	Anderson et al.	May 2011	A1
20110153938	Verzunov et al.	Jun 2011	A1
20110153941	Spatscheck et al.	Jun 2011	A1
20110154318	Oshins et al.	Jun 2011	A1
20110154350	Doyle et al.	Jun 2011	A1
20110161461	Niven-Jenkins	Jun 2011	A1
20110166935	Armentrout et al.	Jun 2011	A1
20110182290	Perkins	Jul 2011	A1
20110191445	Dazzi	Jul 2011	A1
20110191446	Dazzi et al.	Aug 2011	A1
20110191447	Dazzi et al.	Aug 2011	A1
20110191449	Swildens et al.	Aug 2011	A1
20110191459	Joshi	Aug 2011	A1
20110196892	Xia	Aug 2011	A1
20110202705	Hayashi et al.	Aug 2011	A1
20110208876	Richardson et al.	Aug 2011	A1
20110208958	Stuedi et al.	Aug 2011	A1
20110209064	Jorgensen et al.	Aug 2011	A1
20110219120	Farber et al.	Aug 2011	A1
20110219372	Agarwal et al.	Sep 2011	A1
20110238501	Almeida	Sep 2011	A1
20110238793	Bedare et al.	Sep 2011	A1
20110239215	Sugai	Sep 2011	A1
20110252142	Richardson et al.	Sep 2011	A1
20110252143	Baumback et al.	Oct 2011	A1
20110255445	Johnson et al.	Oct 2011	A1
20110258049	Ramer et al.	Oct 2011	A1
20110258614	Tamm	Oct 2011	A1
20110270964	Huang et al.	Oct 2011	A1
20110276623	Girbal	Nov 2011	A1
20110282988	Wang et al.	Nov 2011	A1
20110296053	Medved et al.	Nov 2011	A1
20110295940	Saleem et al.	Dec 2011	A1
20110295942	Raghunath et al.	Dec 2011	A1
20110296370	Ferris et al.	Dec 2011	A1
20110296473	Babic	Dec 2011	A1
20110302304	Baumback et al.	Dec 2011	A1
20110307533	Saeki	Dec 2011	A1
20110320522	Endres et al.	Dec 2011	A1
20110320559	Foti	Dec 2011	A1
20120005673	Cervantes et al.	Jan 2012	A1
20120011190	Driesen et al.	Jan 2012	A1
20120011509	Husain	Jan 2012	A1
20120014249	Mao et al.	Jan 2012	A1
20120023226	Petersen et al.	Jan 2012	A1
20120036238	Sundaram et al.	Jan 2012	A1
20120031626	Clayton et al.	Feb 2012	A1
20120041899	Greene et al.	Feb 2012	A1
20120041970	Ghosh et al.	Feb 2012	A1
20120042381	Antonakakis et al.	Feb 2012	A1
20120054860	Wyschogrod et al.	Feb 2012	A1
20120066360	Ghosh	Mar 2012	A1
20120066681	Levy	Mar 2012	A1
20120072600	Richardson et al.	Mar 2012	A1
20120072608	Peters et al.	Mar 2012	A1
20120078998	Son et al.	Mar 2012	A1
20120079096	Cowan et al.	Mar 2012	A1
20120079115	Richardson et al.	Mar 2012	A1
20120089700	Safruti et al.	Mar 2012	A1
20120023090	Holloway et al.	Apr 2012	A1
20120089972	Scheidel et al.	Apr 2012	A1
20120096065	Suit et al.	Apr 2012	A1
20120096166	Devarapalli et al.	Apr 2012	A1
20120110515	Abramoff et al.	Apr 2012	A1
20120117621	Kondamuru et al.	May 2012	A1
20120124184	Sakata et al.	May 2012	A1
20120131177	Brandt et al.	May 2012	A1
20120136697	Peles et al.	May 2012	A1
20120142310	Pugh et al.	May 2012	A1
20120143688	Alexander	Jun 2012	A1
20120159476	Ramteke et al.	Jun 2012	A1
20120166516	Simmons et al.	Jun 2012	A1
20120169646	Berkes et al.	Jun 2012	A1
20120173760	Jog et al.	Jul 2012	A1
20120179796	Nagaraj et al.	Jul 2012	A1
20120179817	Bade et al.	Jul 2012	A1
20120179839	Raciborski et al.	Jul 2012	A1
20120198043	Hesketh et al.	Jul 2012	A1
20120198071	Black et al.	Aug 2012	A1
20120204176	Tian	Aug 2012	A1
20120209942	Zehavi et al.	Aug 2012	A1
20120222005	Harris et al.	Aug 2012	A1
20120224516	Stojanovski et al.	Aug 2012	A1
20120226649	Kovacs et al.	Sep 2012	A1
20120233329	Dickinson et al.	Sep 2012	A1
20120233522	Barton et al.	Sep 2012	A1
20120233668	Leafe et al.	Sep 2012	A1
20120239725	Hartrick et al.	Sep 2012	A1
20120246129	Rothschild et al.	Sep 2012	A1
20120246257	Brown	Sep 2012	A1
20120254961	Kim et al.	Sep 2012	A1
20120257628	Bu et al.	Oct 2012	A1
20120259954	McCarthy et al.	Oct 2012	A1
20120266231	Spiers et al.	Oct 2012	A1
20120272224	Brackman	Oct 2012	A1
20120278229	Vishwanathan et al.	Oct 2012	A1
20120278831	van Coppenolle et al.	Nov 2012	A1
20120278833	Tam	Nov 2012	A1
20120297009	Amir et al.	Nov 2012	A1
20120303785	Sivasubramanian et al.	Nov 2012	A1
20120303804	Sundaram et al.	Nov 2012	A1
20120311648	Swildens et al.	Nov 2012	A1
20120317573	Osogami et al.	Dec 2012	A1
20120324089	Joshi	Dec 2012	A1
20130003547	Motwani et al.	Jan 2013	A1
20130003735	Chao et al.	Jan 2013	A1
20130007100	Trahan et al.	Jan 2013	A1
20130007101	Trahan et al.	Jan 2013	A1
20130007102	Trahan et al.	Jan 2013	A1
20130007241	Trahan et al.	Jan 2013	A1
20130007273	Baumback et al.	Jan 2013	A1
20130013764	Li	Jan 2013	A1
20130018945	Vendrow et al.	Jan 2013	A1
20130019311	Swildens et al.	Jan 2013	A1
20130034099	Hikichi et al.	Jan 2013	A1
20130036307	Gagliano et al.	Feb 2013	A1
20130041872	Aizman et al.	Feb 2013	A1
20130042328	Padinjareveetil	Feb 2013	A1
20130046869	Jenkins et al.	Feb 2013	A1
20130046883	Lientz et al.	Feb 2013	A1
20130054675	Jenkins et al.	Feb 2013	A1
20130055374	Kustarz et al.	Feb 2013	A1
20130067530	Spektor et al.	Feb 2013	A1
20130061306	Sinn	Mar 2013	A1
20130073808	Puthalath et al.	Mar 2013	A1
20130080420	Taylor et al.	Mar 2013	A1
20130080421	Taylor et al.	Mar 2013	A1
20130080576	Taylor et al.	Mar 2013	A1
20130080577	Taylor et al.	Mar 2013	A1
20130080623	Thireault	Mar 2013	A1
20130080627	Kukreja et al.	Mar 2013	A1
20130080636	Friedman et al.	Mar 2013	A1
20130086001	Bhogal et al.	Mar 2013	A1
20130084898	Li et al.	Apr 2013	A1
20130089005	Li et al.	Apr 2013	A1
20130095806	Salkintzis et al.	Apr 2013	A1
20130103834	Dzerve et al.	Apr 2013	A1
20130111035	Alapati et al.	Apr 2013	A1
20130117282	Mugali, Jr. et al.	May 2013	A1
20130117849	Golshan et al.	May 2013	A1
20130130221	Kortemeyer et al.	May 2013	A1
20130132854	Raleigh et al.	May 2013	A1
20130133057	Yoon et al.	May 2013	A1
20130136138	Miller et al.	May 2013	A1
20130151646	Chidambaram et al.	May 2013	A1
20130191499	Ludin et al.	Jul 2013	A1
20130198341	Kim	Aug 2013	A1
20130212300	Eggleston et al.	Aug 2013	A1
20130219020	McCarthy et al.	Aug 2013	A1
20130227165	Liu	Aug 2013	A1
20130227559	Tsirkin	Aug 2013	A1
20130246567	Green et al.	Aug 2013	A1
20130247061	Kiehn	Sep 2013	A1
20130254269	Sivasubramanian et al.	Sep 2013	A1
20130254879	Chesla et al.	Sep 2013	A1
20130263256	Dickinson et al.	Sep 2013	A1
20130268616	Sakata et al.	Oct 2013	A1
20130275549	Field et al.	Oct 2013	A1
20130279335	Ahmadi	Oct 2013	A1
20130283266	Baset et al.	Oct 2013	A1
20130305046	Mankovski et al.	Oct 2013	A1
20130305083	Machida	Nov 2013	A1
20130311555	Laoutaris et al.	Nov 2013	A1
20130311583	Humphreys et al.	Nov 2013	A1
20130311605	Richardson et al.	Nov 2013	A1
20130311989	Ota et al.	Nov 2013	A1
20130318525	Palanisamy	Nov 2013	A1
20130339429	Richardson et al.	Nov 2013	A1
20130346465	Maltz et al.	Dec 2013	A1
20130346470	Obstfeld	Dec 2013	A1
20130346567	Richardson et al.	Dec 2013	A1
20130346614	Baughman et al.	Dec 2013	A1
20140006465	Davis et al.	Jan 2014	A1
20140006577	Joe et al.	Jan 2014	A1
20140007239	Sharpe et al.	Jan 2014	A1
20140013403	Shuster	Jan 2014	A1
20140019605	Boberg	Jan 2014	A1
20140022951	Lemieux	Jan 2014	A1
20140032658	Falls	Jan 2014	A1
20140036675	Wang et al.	Jan 2014	A1
20140040478	Hsu et al.	Feb 2014	A1
20140047104	Rodriguez	Feb 2014	A1
20140053022	Forgette et al.	Feb 2014	A1
20140059198	Richardson et al.	Feb 2014	A1
20140059208	Yan et al.	Feb 2014	A1
20140059379	Ren et al.	Feb 2014	A1
20140082165	Marr et al.	Feb 2014	A1
20140082614	Klein et al.	Mar 2014	A1
20140089917	Attalla et al.	Mar 2014	A1
20140108672	Ou et al.	Mar 2014	A1
20140108474	David et al.	Apr 2014	A1
20140109095	Farkash	Apr 2014	A1
20140122698	Batrouni et al.	Apr 2014	A1
20140119194	Raciborski et al.	May 2014	A1
20140122725	Batrouni et al.	May 2014	A1
20140137111	Dees et al.	May 2014	A1
20140143305	Choi et al.	May 2014	A1
20140149601	Carney et al.	May 2014	A1
20140164817	Bartholomy et al.	May 2014	A1
20140164584	Joe et al.	Jun 2014	A1
20140165061	Greene et al.	Jun 2014	A1
20140172944	Newton et al.	Jun 2014	A1
20140181268	Stevens et al.	Jun 2014	A1
20140195686	Yeager et al.	Jun 2014	A1
20140189069	Gero et al.	Jul 2014	A1
20140200036	Egner et al.	Jul 2014	A1
20140215019	Ahrens	Jul 2014	A1
20140244937	Bloomstein et al.	Aug 2014	A1
20140258523	Kazerani et al.	Sep 2014	A1
20140269371	Badea et al.	Sep 2014	A1
20140279852	Chen	Sep 2014	A1
20140280606	Long	Sep 2014	A1
20140280679	Dey et al.	Sep 2014	A1
20140297870	Eggleston et al.	Sep 2014	A1
20140297866	Ennaji et al.	Oct 2014	A1
20140298021	Kwon et al.	Oct 2014	A1
20140310402	Giaretta et al.	Oct 2014	A1
20140310811	Hentunen	Oct 2014	A1
20140324774	Chen et al.	Oct 2014	A1
20140325155	Marshall et al.	Oct 2014	A1
20140331328	Wang et al.	Oct 2014	A1
20140337472	Newton et al.	Nov 2014	A1
20140351413	Smith et al.	Nov 2014	A1
20140351871	Bomfim et al.	Nov 2014	A1
20150006615	Wainner et al.	Jan 2015	A1
20150019686	Backholm	Jan 2015	A1
20150026407	Mclellan et al.	Jan 2015	A1
20150067171	Yum	Jan 2015	A1
20150036493	CJ et al.	Feb 2015	A1
20150074228	Drake	Mar 2015	A1
20150081877	Sethi et al.	Mar 2015	A1
20150088586	Pavlas et al.	Mar 2015	A1
20150088964	Shiell et al.	Mar 2015	A1
20150088972	Brand et al.	Mar 2015	A1
20150089621	Khalid	Mar 2015	A1
20150095516	Bergman	Mar 2015	A1
20150106864	Li et al.	Apr 2015	A1
20150154051	Kruglick	Apr 2015	A1
20150130813	Taraki	May 2015	A1
20150149600	Thibeault et al.	May 2015	A1
20150149631	Lissack	May 2015	A1
20150156172	Nandi et al.	Jun 2015	A1
20150156279	Vaswani et al.	Jun 2015	A1
20150163273	Radcliffe et al.	Jun 2015	A1
20150180995	Hofmann	Jun 2015	A1
20150188734	Petrov	Jun 2015	A1
20150189042	Sun et al.	Jul 2015	A1
20150200991	Kwon	Jul 2015	A1
20150215388	Kontothanassis et al.	Jul 2015	A1
20150215656	Pulung et al.	Jul 2015	A1
20150242397	Zhuang	Aug 2015	A1
20150244580	Saavedra	Aug 2015	A1
20150264009	Scharber et al.	Sep 2015	A1
20150271031	Beevers	Sep 2015	A1
20150281367	Nygren et al.	Oct 2015	A1
20150288647	Chhabra et al.	Oct 2015	A1
20150317118	Orikasa et al.	Nov 2015	A1
20150339136	Suryanarayanan et al.	Nov 2015	A1
20150341431	Hartrick et al.	Nov 2015	A1
20150358276	Liu et al.	Nov 2015	A1
20150347311	Tanaka et al.	Dec 2015	A1
20150350365	Khakpour et al.	Dec 2015	A1
20150358436	Kim et al.	Dec 2015	A1
20150363113	Rahman et al.	Dec 2015	A1
20150363282	Rangasamy	Dec 2015	A1
20160006645	Rave	Jan 2016	A1
20160006672	Saavedra	Jan 2016	A1
20160021197	Pogrebinsky et al.	Jan 2016	A1
20160028598	Khakpour et al.	Jan 2016	A1
20160028755	Vasseur et al.	Jan 2016	A1
20160036857	Foxhoven et al.	Jan 2016	A1
20160065475	Hilt et al.	Feb 2016	A1
20160072669	Saavedra	Mar 2016	A1
20160104346	Ovalle et al.	Mar 2016	A1
20160132600	Woodhead et al.	Apr 2016	A1
20160142251	Contreras et al.	May 2016	A1
20160182454	Phonsa et al.	May 2016	A1
20160164761	Sathyanarayana et al.	Jun 2016	A1
20160164799	Popli et al.	Jun 2016	A1
20160182542	Staniford	Jun 2016	A1
20160241639	Brookins et al.	Jun 2016	A1
20160253262	Nadgowda	Aug 2016	A1
20160255042	Newton	Sep 2016	A1
20160269927	Kim et al.	Sep 2016	A1
20160274929	King	Sep 2016	A1
20160294678	Khakpour et al.	Sep 2016	A1
20160337426	Shribman et al.	Oct 2016	A1
20160366202	Phillips et al.	Nov 2016	A1
20160373789	Tsukagoshi	Dec 2016	A1
20170024800	Shah	Jan 2017	A1
20170034254	Salkintzis	Feb 2017	A1
20170041333	Mahjoub et al.	Feb 2017	A1
20170041428	Katsev	Feb 2017	A1
20170099345	Leach	Mar 2017	A1
20170099254	Leach et al.	Apr 2017	A1
20170109316	Hack et al.	Apr 2017	A1
20170153980	Araújo et al.	May 2017	A1
20170155678	Araújo et al.	Jun 2017	A1
20170155732	Araújo et al.	Jun 2017	A1
20170163425	Kaliski, Jr.	Jun 2017	A1
20170170973	Gill et al.	Jun 2017	A1
20170171146	Sharma et al.	Jun 2017	A1
20170180217	Puchala et al.	Jun 2017	A1
20170187768	Huang et al.	Jun 2017	A1
20170214761	Hsu et al.	Jun 2017	A1
20170257340	Richardson et al.	Jul 2017	A1
20170223029	Sharma et al.	Aug 2017	A1
20170374121	Phillips et al.	Dec 2017	A1
20180011913	Kapanipathi et al.	Jan 2018	A1
20180027040	Bae	Jan 2018	A1
20180077109	Hoeme et al.	Jan 2018	A1
20180063193	Chandrashekhar et al.	Mar 2018	A1
20180077110	Huston, III et al.	Mar 2018	A1
20180088993	Gerdesmeier	Mar 2018	A1
20180101324	Sharma	Apr 2018	A1
20180173526	Prinsloo et al.	Jun 2018	A1
20180176615	Hannu et al.	Jun 2018	A1
20180191817	Richardson et al.	Jun 2018	A1
20180337885	Singh et al.	Nov 2018	A1
20190020562	Richardson et al.	Jan 2019	A1
20190028562	Watson et al.	Jan 2019	A1
20190044787	Richardson et al.	Jan 2019	A1
20190044846	Howard et al.	Feb 2019	A1
20190052518	Gal et al.	Feb 2019	A1
20190074982	Hughes	Mar 2019	A1
20190089818	Choi	Mar 2019	A1
20190098109	Watson	Mar 2019	A1
20190121739	Richardson et al.	Apr 2019	A1
20190129908	Kumarasamy	Apr 2019	A1
20190140922	Ellsworth et al.	May 2019	A1
20190173941	Puchala et al.	May 2019	A1
20190173972	MacCarthaigh et al.	Jun 2019	A1
20190222666	Uppal et al.	Jun 2019	A1
20190297137	Richardson et al.	Sep 2019	A1
20190032751	Kalagi et al.	Oct 2019	A1
20200065132	Mercier et al.	Feb 2020	A1
20200084268	Hollis et al.	Mar 2020	A1
20200193234	Pai et al.	Jun 2020	A1
20200195677	Uppal et al.	Jun 2020	A1
20200195753	Richardson et al.	Jun 2020	A1
20200265096	Raftery	Aug 2020	A1
20200287817	Howard et al.	Sep 2020	A1
20200366638	Vasquez et al.	Nov 2020	A1
20200389534	Sivasubramanian et al.	Dec 2020	A1
20200389541	Baldwin et al.	Dec 2020	A1
20210021692	Richardson et al.	Jan 2021	A1
20210119961	Thunga et al.	Apr 2021	A1
20210184958	Kolar et al.	Jun 2021	A1
20210185114	Baldwin et al.	Jun 2021	A1
20210194806	Richardson et al.	Jun 2021	A1
20210297365	Richardson et al.	Sep 2021	A1
20210367832	Ramachandran et al.	Nov 2021	A1
20220017401	Richardson et al.	Jun 2022	A1
20220224767	Watson	Jul 2022	A1
20220272146	Richardson et al.	Aug 2022	A1
20220407933	Swaminathan	Dec 2022	A1

Foreign Referenced Citations (51)

Number	Date	Country
2741 895	May 2010	CA
2765397	Feb 2011	CA
1422468	Jun 2003	CN
1511399	Jul 2004	CN
1605182	Apr 2005	CN
101189598	May 2008	CN
101431539	May 2009	CN
101460907	Jun 2009	CN
101631133	Jan 2010	CN
103152357	Jun 2013	CN
103731481	Apr 2014	CN
104995935	Oct 2015	CN
60318825	Jan 2009	DE
1603307	Dec 2005	EP
1351141	Oct 2007	EP
2008167	Dec 2008	EP
3156911	Apr 2017	EP
07-141305	Jun 1995	JP
2001-0506093	May 2001	JP
2001-249907	Sep 2001	JP
2002-024192	Jan 2002	JP
2002-044137	Feb 2002	JP
2002-323986	Nov 2002	JP
2003-167810	Jun 2003	JP
2003-167813	Jun 2003	JP
2003-188901	Jul 2003	JP
2003-522358	Jul 2003	JP
2004-070935	Mar 2004	JP
2004-532471	Oct 2004	JP
2004-533738	Nov 2004	JP
2005-537687	Dec 2005	JP
3748216	Feb 2006	JP
2007-133896	May 2007	JP
2007-207225	Aug 2007	JP
2008-515106	May 2008	JP
2009-071538	Apr 2009	JP
2012-509623	Apr 2012	JP
2012-209623	Oct 2012	JP
WO 2001045349	Jun 2001	WO
WO 2002069608	Sep 2002	WO
WO 2005071560	Aug 2005	WO
WO 2007007960	Jan 2007	WO
WO 2007126837	Nov 2007	WO
WO 2009124006	Oct 2009	WO
WO 2010002603	Jan 2010	WO
WO 2012044587	Apr 2012	WO
WO 2012065641	May 2012	WO
WO 2014047073	Mar 2014	WO
WO 2015119606	Aug 2015	WO
WO 2017106455	Jun 2017	WO
WO 2018236597	Dec 2018	WO

Non-Patent Literature Citations (192)

Entry
Office Action in Chinese Application No. 201810426428.0 dated Jul. 20, 2020 in 25 pages.
Second Office Action in Chinese Application No. 201610828846.3 dated Aug. 5, 2020.
Office Action issued in connection with European Patent Application No. 18734734 dated Oct. 19, 2020.
Examination Report in Indian Application No. 201918034730 dated Mar. 9, 2022 in 7 pages.
Communication regarding the expiry of the time limit within which notice of opposition may be filed in Application No. 16876655.8 dated Jun. 29, 2022.
“Non-Final Office Action dated Jan. 3, 2012,” U.S. Appl. No. 12/652,541; dated Jan. 3, 2012; 35 pages.
“Final Office Action dated Sep. 5, 2012,” U.S. Appl. No. 12/652,541; dated Sep. 5, 2012; 40 pages.
“Notice of Allowance dated Jan. 4, 2013,” U.S. Appl. No. 12/652,541; dated Jan. 4, 2013; 11 pages.
“Non-Final Office Action dated Apr. 30, 2014,” U.S. Appl. No. 13/842,970; 20 pages.
“Final Office Action dated Aug. 19, 2014,” U.S. Appl. No. 13/842,970; 13 pages.
“Notice of Allowance dated Dec. 5, 2014,” U.S. Appl. No. 13/842,970; 6 pages.
Canonical Name (CNAME) DNS Records, domainavenue.com, Feb. 1, 2001, XP055153783, Retrieved from the Internet: URL:http://www.domainavenue.com/cname.htm [retrieved on Nov. 18, 2014].
“Content delivery network”, Wikipedia, the free encyclopedia, Retrieved from the Internet: URL:http://en.wikipedia.org/w/index.php?title=Contentdelivery network&oldid=601009970, XP055153445, Mar. 24, 2008.
“Global Server Load Balancing with Serverlron,” Foundry Networks, retrieved Aug. 30, 2007, from http://www.foundrynet.com/pdf/an-global-server-load-bal.pdf, 7 pages.
“Grid Computing Solutions,” Sun Microsystems, Inc., retrieved May 3, 2006, from http://www.sun.com/software/grid, 3 pages.
“Grid Offerings,” Java.net, retrieved May 3, 2006, from http://wiki.java.net/bin/view/Sungrid/OtherGridOfferings, 8 pages.
“Recent Advances Boost System Virtualization,” eWeek.com, retrieved from May 3, 2006, http://www.eWeek.com/article2/0,1895,1772626,00.asp, 5 pages.
“Scaleable Trust of Next Generation Management (STRONGMAN),” retrieved May 17, 2006, from http://www.cis.upenn.edu/˜dsl/STRONGMAN/, 4 pages.
“Sun EDA Compute Ranch,” Sun Microsystems, Inc., retrieved May 3, 2006, from http://sun.com/processors/ranch/brochure.pdf, 2 pages.
“Sun Microsystems Accelerates UltraSP ARC Processor Design Program With New Burlington, Mass. Compute Ranch,” Nov. 6, 2002, Sun Microsystems, Inc., retrieved May 3, 2006, from http://www.sun.com/smi/Press/sunflash/2002-11/sunflash.20021106.3 .xml, 2 pages.
“Sun N1 Grid Engine 6,” Sun Microsystems, Inc., retrieved May 3, 2006, from http://www.sun.com/software/gridware/index.xml, 3 pages.
“Sun Opens New Processor Design Compute Ranch,” Nov. 30, 2001, Sun Microsystems, Inc., retrieved May 3, 2006, from http://www.sun.com/smi/Press/sunflash/2001-11/sunflash.20011130.1.xml, 3 pages.
“The Softricity Desktop,” Softricity, Inc., retrieved May 3, 2006, from http://www.softricity.com/products/, 3 pages.
“Xen—The Xen virtual Machine Monitor,” University of Cambridge Computer Laboratory, retrieved Nov. 8, 2005, from http://www.cl.cam.ac.uk/Research/SRG/netos/xen/, 2 pages.
“XenFaq,” retrieved Nov. 8, 2005, from http://wiki.xensource.com/xenwiki/XenFaq?action=print, 9 pages.
Abi, Issam, et al., “A Business Driven Management Framework for Utility Computing Environments,” Oct. 12, 2004, HP Laboratories Bristol, HPL-2004-171, retrieved Aug. 30, 2007, from http://www.hpl.hp.com/techreports/2004/HPL-2004-171.pdf, 14 pages.
American Bar Association; Digital Signature Guidelines Tutorial [online]; Feb. 10, 2002 [retrieved on Mar. 2, 2010]; American Bar Association Section of Science and Technology Information Security Committee; Retrieved from the internet: (URL: http://web.archive.org/web/20020210124615/www.abanet.org/scitech/ec/isc/dsg-tutorial.html; pp. 1-8.
Arends et al., DNS Security Introduction and Requirements, RFC 4033, Mar. 2005, 21 pages.
Ariyapperuma et al., “Security Vulnerabilities in DNS and DNSSEC.” The Second International Conference on Availability, Reliability and Security, IEEE, 2007, 8 pages.
Armour et al.: “A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities”; Management Science, vol. 9, No. 2 (Jan. 1963); pp. 294-309.
Baglioni et al., “Preprocessing and Mining Web Log Data for Web Personalization”, LNAI 2829, 2003, pp. 237-249.
Barbir, A., et al., “Known Content Network (CN) Request-Routing Mechanisms”, Request for Comments 3568, [online], IETF, Jul. 2003, [retrieved on Feb. 26, 2013], Retrieved from the Internet: (URL: http://tools.ietf.org/rfc/rfc3568.txt).
Bellovin, S., “Distributed Firewalls,”;login;:37-39, Nov. 1999, http://www.cs.columbia.edu/-smb/papers/distfw. html, 10 pages, retrieved Nov. 11, 2005.
Blaze, M., “Using the KeyNote Trust Management System,” Mar. 1, 2001, from http://www.crypto.com/trustmgt/kn.html, 4 pages, retrieved May 17, 2006.
Brenton, C., “What is Egress Filtering and How Can I Implement It?—Egress Filtering v 0.2,” Feb. 29, 2000, SANS Institute, http://www.sans.org/infosecFAQ/firewall/egress.htm, 6 pages.
Byun et al., “A Dynamic Grid Services Deployment Mechanism for On-Demand Resource Provisioning”, IEEE International Symposium on Cluster Computing and the Grid:863-870, 2005.
Chandramouli et al., “Challenges in Securing the Domain Name System.” IEEE Security & Privacy4.1 (2006),pp. 84-87.
Chipara et al, “Realtime Power-Aware Routing in Sensor Network”, IEEE, 2006, 10 pages.
Clark, C., “Live Migration of Virtual Machines,” May 2005, NSDI '05: 2nd Symposium on Networked Systems Design and Implementation, Boston, MA, May 2-4, 2005, retrieved from http://www.usenix.org/events/nsdi05/tech/full_papers/clark/clark.pdf, 14 pages.
Cohen et al., “Proactive Caching of DNS Records: Addressing a Performance Bottleneck”, Proceedings of Saint 2001 Symposium on Applications and the Internet; 8-12, Jan. 8, 2001, IEEE Computer Society, pp. 85-94.
Coulson, D., “Network Security Iptables,” Apr. 2003, Linuxpro, Part 2, retrieved from http://davidcoulson.net/writing/lxf/38/iptables.pdf, 4 pages.
Coulson, D., “Network Security Iptables,” Mar. 2003, Linuxpro, Part 1, retrieved from http://davidcoulson.net/writing/lxf/39/iptables.pdf, 4 pages.
Deleuze, C., et al., A DNS Based Mapping Peering System for Peering CDNs, draft-deleuze-cdnp-dnsmap-peer-00.txt, Nov. 20, 2000, 20 pages.
Demers, A., “Epidemic Algorithms For Replicated Database Maintenance,” 1987, Proceedings of the sixth annual ACM Symposium on Principles of Distributed Computing, Vancouver, British Columbia, Canada, Aug. 10-12, 1987, 12 pages.
Eastlake, Donald, Domain Name System Security Extensions, RFC 2535, Mar. 1999, 47 pages.
Frangoudis et al., “PTPv2-based network load estimation and its application to QoE monitoring for Over-the-Top services”, IEEE, The 5th International conference on Information, Intelligence, Systems and Applications, USA 2014, XP032629858, Jul. 7, 2014, pp. 176-181.
Gruener, J., “A Vision of Togetherness,” May 24, 2004, NetworkWorld, retrieved May 3, 2006, from, http://www.networkworld.com/supp/2004/ndc3/0524virt.html, 9 pages.
Gunther et al., “Measuring Round Trip Times to determine the Distance between WLAN Nodes”,May 2005, In Proc. of Networking 2005, all pages.
Gunther et al., “Measuring Round Trip Times to determine the Distance between WLAN Nodes”, Dec. 18, 2004, Technical University Berlin, all pages.
Guo, F., Understanding Memory Resource Management in Vmware vSphere 5.0, Vmware, 2011, pp. 1-29.
Hameed, CC, “Disk Fragmentation and System Performance”, Mar. 14, 2008, 3 pages.
Hartung et al., Digital rights management and watermarking of multimedia content for m-commerce applications; Published in: Communications Magazine, IEEE (vol. 38, Issue: 11 ); Date of Publication: Nov. 2000; pp. 78-84; IEEE Xplore.
Horvath et al., “Enhancing Energy Efficiency in Multi-tier Web Server Clusters via Prioritization,” in Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International, vol., No., pp. 1-6, Mar. 26-30, 2007.
Ioannidis, S., et al., “Implementing a Distributed Firewall,” Nov. 2000, (ACM) Proceedings of the ACM Computer and Communications Security (CCS) 2000, Athens, Greece, pp. 190-199, retrieved from http://www.cis.upenn.edu/˜dls/STRONGMAN/Papers/df.pdf, 10 pages.
JH Software, Moving a DNS Server to a New IP Address, last updated Jan. 26, 2006, 1 page.
Joseph, Joshy, et al., “Introduction to Grid Computing,” Apr. 16, 2004, retrieved Aug. 30, 2007, from http://www.informit.com/articles/printerfriendly.aspx?p=169508, 19 pages.
Kalafut et al., Understanding Implications of DNS Zone Provisioning., Proceeding IMC '08 Proceedings of the 8th AMC SIGCOMM conference on Internet measurement., pp. 211-216., ACM New York, NY, USA., 2008.
Kato, Yoshinobu , Server load balancer—Difference in distribution technique and supported protocol—Focus on function to meet the needs, Nikkei Communications, Japan, Nikkei Business Publications, Inc., Mar. 20, 2000, vol. 314, pp. 114 to 123.
Kenshi, P., “Help File Library: Iptables Basics,” Justlinux, retrieved Dec. 1, 2005, from http://www.justlinux.com/nhf/Security/lptables _ Basics.html, 4 pages.
Krsul et al., “VMPIants: Providing and Managing Virtual Machine Execution Environments for Grid Computing”, Nov. 6, 2004 (Nov. 6, 2004), Supercomputing, 2004. Proceedings of the ACM/IEEE SC2004 Conference Pittsburgh, PA, USA Nov. 6-12, 2004, Piscataway, NJ, USA, IEEE, 1730 Massachusetts Ave., NW Washington, DC 20036-1992 USA, 12 pages.
Liu, “The Ultimate Guide to Preventing DNS-based DDoS Attacks”, Retrieved from http://www.infoworld.com/article/2612835/security/the-ultimate-guide-to-preventing-dns-based-ddos-attacks.html, Published Oct. 30, 2013.
Liu et al., “Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests,” Data & Knowledge Engineering 61 (2007) pp. 304-330.
Maesono, et al., “A Local Scheduling Method considering Data Transfer in Data Grid,” Technical Report of IEICE, vol. 104, No. 692, pp. 435-440, The Institute of Electronics, Information and Communication Engineers, Japan, Feb. 2005.
Meng et al., “Improving the Scalability of Data Center Networks with Traffic-Aware Virtual Machine Placement”; Proceedings of the 29th Conference on Information Communications, INFOCOM'10, pp. 1154-1162. Piscataway, NJ. IEEE Press, 2010.
Mulligan et al.; How DRM-based content delivery systems disrupt expectations of “personal use”; Published in: Proceeding DRM '03 Proceedings of the 3rd ACM workshop on Digital rights management; 2003; pp. 77-89; ACM Digital Library.
Ragan, “Three Types of DNS Attacks and How to Deal with Them”, Retrieved from http://www.csoonline.com/article/2133916/malware-cybercrime/three-types-of-dns-attacks-and-how-to-deal-with-them.html, Published Aug. 28, 2013.
Shankland, S., “Sun to buy start-up to bolster N1 ,” Jul. 30, 2003, CNet News.com, retrieved May 3, 2006, http://news.zdnet.com/2100-3513_22-5057752.html, 8 pages.
Sharif et al., “Secure In-VM Monitoring Using Hardware Virtualization”, Microsoft, Oct. 2009 http://research.microsoft.com/pubs/153179/sim-ccs09.pdf; 11 pages.
Strand, L., “Adaptive distributed firewall using intrusion detection,” Nov. 1, 2004, University of Oslo Department of Informatics, retrieved Mar. 8, 2006, from http://gnist.org/˜lars/studies/master/StrandLars-master.pdf, 158 pages.
Takizawa, et al., “Scalable MultiReplication Framework on the Grid,” Report of Study of Information Processing Society of Japan, Information Processing Society, vol. 2004, No. 81, pp. 247-252, Japan, Aug. 1, 2004.
Tan et al., “Classification: Basic Concepts, Decision Tree, and Model Evaluation”, Introduction in Data Mining; http://www-users.cs.umn.edu/˜kumar/dmbook/ch4.pdf, 2005, pp. 245-205.
Van Renesse, R., “Astrolabe: A Robust and Scalable Technology for Distributed System Monitoring, Management, and Data Mining,” May 2003, ACM Transactions on Computer Systems (TOCS), 21 (2): 164-206, 43 pages.
Vijayan, J., “Terraspring Gives Sun's N1 a Boost,” Nov. 25, 2002, Computerworld, retrieved May 3, 2006, from http://www.computerworld.com/printthis/2002/0,4814, 76159,00.html, 3 pages.
Virtual Iron Software Home, Virtual Iron, retrieved May 3, 2006, from http://www.virtualiron.com/, 1 page.
Waldspurger, CA., “Spawn: A Distributed Computational Economy,” Feb. 1992, IEEE Transactions on Software Engineering, 18(2): 103-117, 15 pages.
Watanabe, et al., “Remote Program Shipping System for GridRPC Systems,” Report of Study of Information Processing Society of Japan, Information Processing Society, vol. 2003, No. 102, pp. 73-78, Japan, Oct. 16, 2003.
Xu et al., “Decision tree regression for soft classification of remote sensing data”, Remote Sensing of Environment 97 (2005) pp. 322-336.
Yamagata, et al., “A virtual-machine based fast deployment tool for Grid execution environment,” Report of Study of Information Processing Society of Japan, Information Processing Society, vol. 2006, No. 20, pp. 127-132, Japan, Feb. 28, 2006.
Zaman et al., “Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds”, Department of Computer Science, Wayne State University, Sep. 2011 http://www.cs.wayne.edu/-dgrosu/pub/ccgrid12-symp.pdf.
Zhao et al., “Distributed file system support for virtual machines in grid computing”, Jun. 4, 2004 (Jun. 4, 2004), High Performance Distributed Computing, 2004. Proceedings. 13th IEEE International Symposium on Honolulu, HI, USA Jun. 4-6, 2004, Piscataway, NJ, USA, IEEE, pp. 202-211.
Zhu, Xiaoyun, et al., “Utility-Driven Workload Management Using Nested Control Design,” Mar. 29, 2006, HP Laboratories Palo Alto, HPL-2005-193(R.1), retrieved Aug. 30, 2007, from http://www.hpl.hp.com/techreports/2005/HPL-20Q5-193R1.pdf, 9 pages.
Supplementary European Search Report in Application No. 09729072.0 2266064 dated Dec. 10, 2014.
Office Action in Application No. 09729072.0 dated May 14, 2018.
Office Action in Application No. 09729072.0 dated Dec. 7, 2018.
First Singapore Written Opinion in Application No. 201006836-9, dated Oct. 12, 2011 in 12 pages.
Singapore Written Opinion in Application No. 201006836-9, dated Apr. 30, 2012 in 10 pages.
First Office Action in Chinese Application No. 200980111422.3 dated Apr. 13, 2012.
First Office Action in Japanese Application No. 2011-502138 dated Feb. 1, 2013.
Singapore Written Opinion in Application No. 201006837-7, dated Oct. 12, 2011 in 11 pages.
Supplementary European Search Report in Application No. 09727694.3 dated Jan. 30, 2012 in 6 pages.
Singapore Examination Report in Application No. 201006837-7 dated Mar. 16, 2012.
First Office Action in Chinese Application No. 200980111426.1 dated Feb. 16, 2013.
Second Office Action in Chinese Application No. 200980111426.1 dated Dec. 25, 2013.
Third Office Action in Chinese Application No. 200980111426.1 dated Jul. 7, 2014.
Fourth Office Action in Chinese Application No. 200980111426.1 dated Jan. 15, 2015.
Fifth Office Action in Chinese Application No. 200980111426.1 dated Aug. 14, 2015.
First Office Action in Japanese Application No. 2011-502139 dated Nov. 5, 2013.
Decision of Rejection in Application No. 2011-502139 dated Jun. 30, 2014.
Office Action in Japanese Application No. 2011-502139 dated Aug. 17, 2015.
Office Action in Indian Application No. 5937/CHENP/2010 dated Jan. 19, 2018.
Singapore Written Opinion in Application No. 201006874-0, dated Oct. 12, 2011 in 10 pages.
First Office Action in Japanese Application No. 2011-502140 dated Dec. 7, 2012.
First Office Action in Chinese Application No. 200980119995.0 dated Jul. 6, 2012.
Second Office Action in Chinese Application No. 200980119995.0 dated Apr. 15, 2013.
Examination Report in Singapore Application No. 201006874-0 dated May 16, 2012.
Search Report in European Application No. 09839809.2 dated May 11, 2015.
Office Action in European Application No. 09839809.2 dated Dec. 8, 2016.
Office Action in Indian Application No. 6210/CHENP/2010 dated Mar. 27, 2018.
First Office Action in Chinese Application No. 200980119993.1 dated Jul. 4, 2012.
Second Office Action in Chinese Application No. 200980119993.1 dated Mar. 12, 2013.
Third Office Action in Chinese Application No. 200980119993.1 dated Oct. 21, 2013.
Supplementary European Search Report in Application No. 09728756.9 dated Jan. 8, 2013.
First Office Action in Japanese Application No. 2011-503091 dated Nov. 18, 2013.
Office Action in Japanese Application No. 2014-225580 dated Oct. 26, 2015.
Office Action in Japanese Application No. 2014-225580 dated Oct. 3, 2016.
Search Report and Written Opinion issued in Singapore Application No. 201006873-2 dated Oct. 12, 2011.
Examination Report in Indian Application No. 6213/CHENP/2010 dated May 23, 2018.
First Office Action is Chinese Application No. 200980125551.8 dated Jul. 4, 2012.
First Office Action in Japanese Application No. 2011-516466 dated Mar. 6, 2013.
Second Office Action in Japanese Application No. 2011-516466 dated Mar. 17, 2014.
Decision of Refusal in Japanese Application No. 2011-516466 dated Jan. 16, 2015.
Office Action in Japanese Application No. 2011 -516466 dated May 30, 2016.
Office Action in Japanese Application No. 2011-516466 dated Mar. 6, 2017.
Office Action in Canadian Application No. 2726915 dated May 13, 2013.
First Office Action in Korean Application No. 10-2011-7002461 dated May 29, 2013.
First Office Action in Chinese Application No. 200980145872.4 dated Nov. 29, 2012.
First Office Action in Canadian Application No. 2741895 dated Feb. 25, 2013.
Second Office Action in Canadian Application No. 2741895 dated Oct. 21, 2013.
Partial Supplementary Search Report in European Application No. 09826977.2 dated Oct. 4, 2016.
Extended Search Report in European Application No. 19184826.6 dated Jan. 17, 2020.
Search Report and Written Opinion in Singapore Application No. 201103333-9 dated Nov. 19, 2012.
Examination Report in Singapore Application No. 201103333-9 dated Aug. 13, 2013.
Office Action in Chinese Application No. 201310717573.1 dated Jul. 29, 2016.
Office Action in European Application No. 11767118.0 dated Feb. 3, 2017.
Office Action in European Application No. 11767118.0 dated Jul. 25, 2018.
Office Action in European Application No. 11767118.0 dated Jan. 29, 2019.
International Search Report and Written Opinion in PCT/US2011/053302 dated Nov. 28, 2011 in 11 pages.
International Preliminary Report on Patentability in PCT/US2011/053302 dated Apr. 2, 2013.
First Office Action in Japanese Application No. 2013-529454 dated Feb. 3, 2014 in 6 pages.
Office Action in Japanese Application No. 2013-529454 dated Mar. 9, 2015 in 8 pages.
First Office Action issued in Australian Application No. 2011307319 dated Mar. 6, 2014 in 5 pages.
Search Report and Written Opinion in Singapore Application No. 201301573-0 dated Jul. 1, 2014.
First Office Action in Chinese Application No. 201180046104.0 dated Nov. 3, 2014.
Second Office Action in Chinese Application No. 201180046104.0 dated Sep. 29, 2015.
Third Office Action in Chinese Application No. 201180046104.0 dated Apr. 14, 2016.
Decision of Rejection in Chinese Application No. 201180046104.0 dated Oct. 17, 2016.
Examination Report in Singapore Application No. 201301573-0 dated Dec. 22, 2014.
Examination Report in Indian Application No. 3105/DELNP/2013, dated Feb. 19, 2019.
International Preliminary Report on Patentability in PCT/US2011/061486 dated May 22, 2013.
International Search Report and Written Opinion in PCT/US2011/061486 dated Mar. 30, 2012 in 11 pages.
Office Action in Canadian Application No. 2816612 dated Nov. 3, 2015.
Office Action in Canadian Application No. 2816612 dated Oct. 7, 2016.
Office Action in Canadian Application No. 2816612 dated Aug. 8, 2017.
First Office Action in Chinese Application No. 201180053405.6 dated Feb. 10, 2015.
Second Office Action in Chinese Application No. 201180053405.6 dated Dec. 4, 2015.
Office Action in Japanese Application No. 2013-540982 dated Jun. 2, 2014.
Written Opinion in Singapore Application No. 201303521-7 dated May 20, 2014.
Extended Search Report in European Application No. 18156163 dated Sep. 3, 2018.
Examination Report in Indian Application No. 4487/DELNP/2013 dated Dec. 28, 2018.
Office Action in Japanese Application No. 2015-533132 dated Apr. 25, 2016.
Office Action in Canadian Application No. 2884796 dated Apr. 28, 2016.
Office Action in Russian Application No. 2015114568 dated May 16, 2016.
Supplementary Examination Report in Singapore Application No. 11201501987U dated May 17, 2017.
Office Action in Chinese Application No. 2013800492635 dated Aug. 30, 2017.
Office Action in European Application No. 13770602.4 dated Mar. 11, 2019.
Office Action in Indian Application No. 2823/DELNP/2015 dated Oct. 25, 2019.
Office Action in Brazilian Application No. BR112015005588-5 dated Jan. 14, 2020.
International Search Report and Written Opinion in PCT/US07/07601 dated Jul. 18, 2008 in 11 pages.
International Preliminary Report on Patentability in PCT/US2007/007601 dated Sep. 30, 2008 in 8 pages.
Supplementary European Search Report in Application No. 07754164.7 dated Dec. 20, 2010 in 7 pages.
Office Action in Chinese Application No. 200780020255.2 dated Mar. 4, 2013.
Office Action in Chinese Application No. 201310537815.9 dated Feb. 1, 2018.
Office Action in Indian Application No. 3742/KOLNP/2008 dated Nov. 22, 2013.
Office Action in Japanese Application No. 2012-052264 dated Dec. 11, 2012 in 26 pages.
Office Action in Japanese Application No. 2013-123086 dated Apr. 15, 2014 in 3 pages.
Office Action in Japanese Application No. 2013-123086 dated Dec. 2, 2014 in 4 pages.
Office Action in Japanese Application No. 2015-075644 dated Apr. 5, 2016.
Office Action in European Application No. 07754164.7 dated Dec. 14, 2015.
Office Action in European Application No. 07754164.7 dated Jan. 25, 2018.
Office Action in Chinese Application No. 201310537815.9 dated Jul. 5, 2016.
Office Action in Chinese Application No. 201310537815.9 dated Jun. 2, 2017.
International Search Report and Written Opinion in PCT/US/2016/ 066848 dated May 1, 2017.
International Preliminary Reporton Patentability in PCT/US/2016/ 066848 dated Jun. 19, 2018.
Extended European Search Report in Application No. 16876655.8 dated Aug. 20, 2019.
Partial Search Report in European Application No. 16876655.8 dated May 15, 2019.
International Search Report and Written Opinion in PCT/US2017/055156 dated Dec. 13, 2017.
International Preliminary Report on Patentability and Written Opinion in PCT/US2017/055156 dated Apr. 9, 2019.
International Search Report and Written Opinion in PCT/US2018/036634 dated Sep. 11, 2018.
International Preliminary Report on Patentability and Written Opinion in PCT/US2018/036634 dated Dec. 24, 2019.
Supplementary Examination Report in Singapore Application No. 10201705920S dated Dec. 24, 2021.
First Office Action in Chinese Applicaton No. 201610112984.1 dated Mar. 20, 2018.
Second Office Action in Chinese Applicaton No. 201610112984.1 dated Feb. 2, 2019.

Related Publications (1)

	Number	Date	Country
	20210042163 A1	Feb 2021	US

Continuations (1)

	Number	Date	Country
Parent	15391673	Dec 2016	US
Child	17081756		US

Multi-region request-driven code execution system

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

US

International Classifications

Disclaimer

Term Extension

Abstract