Streaming analytics using a serverless compute 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.

One example use of data centers is to process or analyze large data sets, which may be impractical to analyze using a single computing device. A specific type of data analytics is streaming data analytics, which conducts processing or analysis on a data stream. In this context, a data “stream” is a set of data that is periodically or continuously updated, rather than being available as a collection. A common goal of streaming analytics is to process data in “real time”— that is, as it is added to the stream, with minimal delay. Thus, streaming analytics may be used to keep real time statistics of data points on a stream, as opposed to, e.g., waiting for all data points to exist before conducting a statistical analysis.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustrative visualization of data streams against which streaming analytics can be conducted, including visualizations of windows and aggregation groups applied to the data streams for purposes of conducting streaming analytics;

FIG. 2 is a block diagram depicting an illustrative environment in which streaming analytics can be conducted against a data stream by invocation of serverless functions on a serverless compute system;

FIG. 3 depicts a general architecture of a computing device providing a polling device of FIG. 2 configured to invoke serverless functions to conduct streaming analytics against a data stream;

FIG. 4 is a flow diagram depicting illustrative interactions for initiating streaming analytics against a data stream, including specification of serverless functions to be invoked to conduct such streaming analytics;

FIG. 5 is a flow diagram depicting illustrative interactions for initiating an aggregation function on a serverless compute system to conduct analysis regarding data items within a data stream;

FIG. 6 is a flow diagram depicting illustrative interactions for initiating a destination function on a serverless compute system to use state information obtained from invocations of an aggregation function to provide a result of streaming analysis conducted regarding data items within a data stream; and

FIG. 7 is an illustrative routine for conducting streaming analytics against a data stream by invocation of serverless functions on a serverless compute system.

DETAILED DESCRIPTION

Generally described, aspects of the present disclosure relate to conducting streaming analytics on a serverless compute system. More specifically, the present disclosure enables code executions on a serverless compute system to analyze items (e.g., “messages”) within a data stream, and maintain “running” calculations regarding those items, such as counts, averages, and the like. The specific analysis may be established within user-defined code, and thus tailored to the needs of an individual user. Embodiments of the present disclosure can enable a user to specify various criteria for streaming analysis, such as time windows over which analysis is to be conducted, a maximum number of data size of items to be analyzed, and the like. These embodiments can then facilitate submission of items within the stream to a serverless compute system, such that analysis—as specified within user defined code—is conducted according to the various criteria established by the user. In this way, embodiments of the present disclosure can facilitate rapid development and deployment of streaming analytics.

As described herein, a serverless compute system (which may also be referred to as a “serverless code execution system” or an “on-demand code execution system”) enables rapid execution of code, which may be supplied by users of the serverless compute system. On submitting code, the serverless compute system can then enable the user to submit “calls” or “invokes” to execute the code, at which point the serverless compute system will generate an execution environment for the code and execute the code within the environment to provide the desired functionality. The environment can then be destroyed shortly after providing the desired functionality, such that a user is responsible only for resources used during execution. These execution times are often very short, making serverless computing highly efficient, particularly for tasks with varying demand levels. However, because the serverless compute system (and not the end user) generally handles management of execution environments, including selection of a host device on which to place the environment, an end user is generally not enabled to guarantee that a particular invocation will result in execution in a particular environment. For this reason, serverless executions are often designed or even restricted to being stateless—such that the result of one execution of code does not depend on processing done during a prior execution of the code.

In the context of streaming analytics, the stateless nature of serverless executions can be problematic, as many streaming analyses specifically depend on state. For example, conducting a running count or average of data items in a stream requires a system, in evaluating one or more new data items, to have knowledge of state regarding counts or averages of older data items. While the system may instead only process data items in batches (e.g., without regard to state related to prior items), this effectively transforms the analysis to batch, rather than streaming, analysis. Accordingly, in a default configuration, it may not be possible conduct streaming analytics on a serverless compute system.

Embodiments of the present disclosure address the above-noted issues by enabling typically stateful processing, such as streaming analytics, to be implemented within stateless execution environments, such as those provided by a serverless compute system. As will be described in more detail below, embodiments of the present disclosure enable an intermediary device to maintain state information related to iterative data processing (e.g., streaming analytics), and to submit the state information in each request to execute code on a serverless compute system. The intermediary device can obtain updated state information in response to each call, and include the updated state information in a next call. In this manner, execution environments on the serverless compute system are relieved of an obligation to maintain state information, and may continue to operate statelessly. However, state information can nevertheless be maintained while processing a set of data, enabling successful stateful analysis of streaming data. As will be described below, the intermediary device may be configured to ensure resiliency of operation, such that failures within a system processing a data set can be identified and corrected. Moreover, the intermediary device can be configured to ensure efficient resiliency, such that providing resiliency does not have a substantial negative impact on the ability of the system to process streaming data.

In one embodiment, the intermediary device is a poller device, which operates to retrieve items from a data stream, and to pass the items for processing to a code execution on a serverless compute system. Illustratively, a poller device in accordance with embodiments of the present disclosure may determine initial state information for processing data items on the stream (e.g., as a null state), retrieve an initial set of items from the stream, and iteratively submit those items to the serverless compute system along with current state information representing a state of processing of the stream. The poller device can be configured to receive in response to each call updated state information, which information can be included within subsequent calls. Because the state information for the stream is passed in each call, environments on serverless compute system are not themselves required to maintain state information. For this reason, no affinity is generally required between a poller device and an environment in which a call is processed. Rather, the serverless compute system can route calls from the poller device to any suitable environment, increasing flexibility of the serverless compute system in executing code corresponding to the call. The poller device can be configured to periodically save the state information to a resilient storage system (e.g., a network storage location with built-in redundancy), and to resume processing based on that saved state information in the event of a failure. Thus, maintaining state information at a poller device provides an efficient mechanism for enabling stateful data processing at a serverless compute system.

While other mechanisms for enabling stateful data processing at a serverless compute system are contemplated herein, these other mechanisms are generally less desirable than maintaining state information at an intermediary (e.g., poller) device. For example, it is conceivable that a serverless compute system is configured to provide affinity for multiple calls to execute a given set of code, such that each call is routed to the same execution environment. It is further conceivable that a serverless compute system enables each such environment to maintain local state information, thus enabling stateful execution of code within the environment. However, this approach significantly decreases flexibility of operation of the serverless compute system, requiring that the system maintain execution environments for long periods of time. Moreover, this approach may not be well suited to address issues that frequently arise in distributed processing systems, such as the need to provide resiliency of operation or the need to scale up or down a number of environments in response to changing operational load. For example, to address these issues, the serverless compute system may be required to frequently save state information of each environment, significantly increasing resource usage of the system. The system may also be required to provide for transferring of state information between environments during scale up or scale down events, increasing the complexity of managing such environments. Another possible mechanism for retaining state information between processing of calls is to configure each execution environment, during processing of a call, to write its state information to a persistent external location, such as network data storage. Thus, subsequent executions may retrieve the state information from the persistent location to facilitate processing of subsequent calls. However, in distributed systems, writing to an external storage location is generally considered a “heavy weight” operation, as it can significantly increase the computing resources used to process a call. For example, writing to a network location may require initiation of a transport control protocol (TCP) session with the network location, a process that can take significant time and resources (in terms of the resources that would otherwise be required to process a single call). Where the number of calls is large (e.g., when high throughput data streams), the additional overhead required for such a heavy weight operation can be significant.

Embodiments of the present disclosure enable state information to be maintained between processing of calls without these drawbacks. For example, an intermediary device can pass state information for a call at the same time that the call is submitted to a serverless compute system, and can receive updated state information as a response to that call. Thus, no additional network communications are required by the serverless compute system. Moreover, the intermediary device can provide resiliency by saving state information periodically, at a periodicity that can be adjusted based on the resources available to the device and overhead required to resume operation in the event of a failure. Specifically, because the intermediary device is capable of a “long term” view of processing of a stream of data items, the device is not required to ensure state information is saved after each call, as the serverless compute system or an external data store might be.

The serverless compute system, as described in detail herein, may provide a network-accessible service enabling users to submit or designate computer-executable code to be executed by virtual machine instances (or other execution environments, such as containers providing operating-system level virtualization) on the serverless compute system. Each set of code on the serverless compute system may define a “task” or “function” and implement specific functionality corresponding to that function when executed on a virtual machine instance of the serverless compute system. Individual implementations of the function on the serverless compute system may be referred to as an “execution” of the function (or a “function execution”). The serverless compute system can further enable users to trigger execution of a function based on a variety of potential events, such as detecting new data at a network-based storage system, transmission of an application programming interface (“API”) call to the serverless compute system, or transmission of a specially formatted hypertext transport protocol (“HTTP”) packet to the serverless compute system. Thus, users may utilize the serverless compute system to execute any specified executable code “on-demand,” without requiring configuration or maintenance of the underlying hardware or infrastructure on which the code is executed. Further, the serverless compute system may be configured to execute functions in a rapid manner (e.g., in under 100 milliseconds [ms]), thus enabling execution of functions in “real-time” (e.g., with little or no perceptible delay to an end user).

Because the serverless compute system can provide the ability to execute a function on-demand, without configuration of an underlying device on which to execute the code, the serverless compute system can provide an excellent platform on which to implement streaming analytics. For example, the serverless compute system can enable a user to implement streaming analytics effectively without regard for the volume of data published to an input data stream, as scaling of compute resources to process the data can be handled by the serverless compute system, rather than being preconfigured by a user. The present disclosure can enable the use of a serverless compute system to conduct streaming analytics by providing an efficient way of maintaining state information for such data analysis, without requiring that such state information be maintained within environments of the serverless compute system or persisted by such environments to additional external locations.

In accordance with embodiments of the present disclosure, a poller device as disclosed herein can, in addition or alternatively to providing a mechanism to maintain state between serverless function invocations, provide a variety of scheduling and work distribution functionalities to enable streaming analytics.

For example, in many cases of streaming analytics, end users may wish to conduct analyses with respect to certain subsets of data on the stream, which may be specified with respect to windows. For example, an end user may wish to analyze items in each window of 30 seconds, 1 minute, 5 minutes, 15 minutes, etc. Windows may be fixed, such that they occur on set intervals. For example, a user may wish to know an average count of errors indicated on a data stream at each 5 minute interval. Additionally or alternatively, windows may be sliding, such that streaming analysis logically considers all possible windows of a given length. For example, an end user may wish to know whether the average count of errors in any possible 5 minute span exceeds a threshold value. Embodiments of the present disclosure can enable a poller device to provide for such windowing. More specifically, a poller device may group items within a data stream into window sets, and pass these window sets to a serverless compute system for processing via a processing function, sometimes referred to herein as an “aggregation” function. The processing function may conduct analysis on the window set, and return state information to the poller device. At the end of each window set, the poller device may pass the final state information for the window into window finalization function, sometimes referred to herein as a “destination” function, which may take action with respect to the final state for the window set, such as by reporting that state to an end user. To facilitate fixed windowing, the poller device may initialize each window based on attribute of each item in the stream, such as a timestamp indicating the time at which the item was added to the stream. For example, to implement 5 minute fixed windows, the poller device may group into a first window any items on the stream with a timestamp between 00:00:00 and 00:05:00 (in HH:MM:SS format, where HH indicates hours, MINI minutes, and SS seconds), cause these items to be processed by an aggregation function to result in state, and at the end of the time window, pass the state to a destination function for final processing. To implement sliding windows, the poller device may create new, potentially overlapping windows for each item on the stream. For example, if an item is added to the stream with a timestamp of 00:00:30, the poller device can initialize a window from 00:00:30 to 00:05:30 (for 5 minute windows) and consider the item as included within that window. If a second item is added to the stream with a timestamp of 00:00:45, the poller device may consider the second item to exist within the first window, and also initialize a second window from 00:00:45 to 00:05:45, with the second item also included within the second window. The poller device can then maintain state information for each window, and, similarly to as noted above, pass items from each window to aggregate and destination functions for processing.

In some instances, the data within a stream during a given window may exceed the capacity of a single function invocation on the serverless compute system. For example, each invocation on the serverless compute system may be limited in computing resources, such as memory, processing cycles, network bandwidth, or the like. In some instances, each invocation on the serverless compute system may be limited in compute time. For example, each invocation may be allowed to execute for no more than a threshold period of time, such as 15 minutes. To address these restrictions, it may be preferable to divide a window set (items in a stream corresponding to a particular time window) for processing. For example, it may be preferable to limit the number of items processed by an instance of an aggregation function, such as by specifying a maximum number of data items, a maximum data size of those items, or the like. In embodiments of the present disclosure, a poller device may provide for such division, by accepting grouping criteria that indicate when to submit a subset of data items from a window set to an aggregation function. Illustratively, if an end user specifies that a maximum of 3 data items are to be processed by each instance of an aggregation function, the poller device may detect when a particular window set has three data items, and submit those data items for processing. In the manner noted above, the poller device may maintain state information resulting from processing the 3 data items, and pass that state information to a next aggregation function invocation for the window set. This batching may continue until all data items for the window set are processed, at which point a destination function for the window can be invoked. As a result, state information for the window can be passed to the destination function, without requiring that the aggregation function support unbounded data input.

To better illustrative scheduling and work distribution functionalities that may be implemented by a poller device as disclosed herein, FIG. 1 shows illustrative sets of data items and relative timing of those data items pushed to three data streams 10, denoted as stream 10A, B, and N in FIG. 1. Each stream includes a number of data items beginning at data item 1 and increasing monotonically. In FIG. 1, the first data item of each stream 10 (data item ‘1’) is shown to the far right, with additional data items joining the stream 10 from the left. Thus, time begins from a given starting point (t=0) and increases to the left. The relative position of each data item in FIG. 1 therefore shows a time of the data item in the stream. Illustratively, the time may be a timestamp added to the data item by operation of data streaming system, indicating time of receipt of the data item, a timestamp field included by an upstream device publishing data items to the stream, etc. In FIG. 1, it is assumed that each stream 10 is subject to the same analysis criteria, including criteria for invoking an aggregation function and window criteria for establishing windows. Specifically, in FIG. 1 it is assumed that fixed windows of a given length are established (corresponding to the dashed lines of FIG. 1), and that an aggregation function supports a maximum of three data items per invocation. In practice, different streams may be associated with different analysis criteria, including different window lengths, different window types (e.g., sliding windows), and different aggregation function limits.

The processing for each stream 10 caused by operation of the poller device is shown in FIG. 1. Specifically, groupings of data items of a stream into an “aggregation” indicate that these data items are passed to an invocation of an aggregation function on a serverless compute system, along with state information maintained for a current window (if any). Illustratively, with respect to stream 10A, the poller device detects three data items during Window A, which corresponds to the maximum number of data items per invocation of the aggregation function. The poller device therefore passes data items 1-3 an invocation of an aggregation function. As these are the first data items in the associated window, the poller device may pass no state to the aggregation function, or may pass an indicator of null state. The poller device additionally detects two other data items—numbers 4 and 5—in the stream 10A. While these data items do not exceed the maximum per-invocation of the aggregation function, they represent the final data items within Window A. Therefore, the poller device submits data items 4 and 5 to the aggregation function, along with state information for the window (e.g., that returned as a result of processing data items 1-3). The poller device then obtains final state information for the window. As the window has closed, the poller device then invokes the destination function for the window, passing to it the final state information for the window. Accordingly, data analytics for the window is completed.

Similar interactions can occur with respect to other streams 10B and 10N (which may represent any number of streams). For example, with respect to stream 10B, the poller device may pass data items 1-3 to an execution of the aggregation function, since the data items represent the maximum number per-invocation of the aggregation function. As no additional data items exist in Window A subsequent to data item 3 of stream 10B, the poller device invokes the destination function with the state information passed back from that aggregation function. With respect to stream 10C, the poller device may detect the end of Window A prior to submitting any data items for processing. The poller device can therefore submit all unprocessed data items (specifically, items 1 and 2) to the aggregation function for processing. After obtaining a result, the poller can pass the result to the destination function as final window state.

Interactions similar to those above can occur for each time window. For example, with respect to stream 10A, aggregation functions can iteratively be called for items 6-8 and 9, with a result of the second aggregation function being passed to a destination function. With respect to streams 10B and 10N, the aggregation function can be called for data items 4-6 and 3-5, respectively, with a result of each aggregation function being passed to the corresponding destination function for the stream. Thus, the poller device can continue to provide analytics for each stream 10 as data items are published to the stream.

While FIG. 1 is described with respect to distinct aggregation and destination functions, in some instances these two functions may be merged. For example, a single function may be provided that implements functionality of both aggregation and destination functions, and accepts as input a flag that distinguishes between these functionalities. Accordingly, the poller device may use a first flag value (e.g., 0) to invoke the function to implement aggregation functionality, and a second flag value (e.g., 1) to invoke the function to implement destination functionality. As another example, a single function may be provided that implements functionality of both aggregation and destination functions, and accepts a flag that indicates whether a current set of data items is the final set for a window. The function may then be configured to process that set of data items (if any), and implement destination functionality for the window. Thus, separate description of aggregation and destination functions should not be viewed to indicate that these functions must be distinct.

As will be appreciated by one of skill in the art in light of the present disclosure, the embodiments disclosed herein improves the ability of computing systems, such as serverless compute systems, to implement streaming data analytics. More specifically, embodiments of the present disclosure enable state information to be efficiently maintained between code executions on serverless computing systems, without requiring that such state information be maintained in an execution environment of the systems. Embodiments of the present disclosure further provide mechanisms for passing data items to serverless code executions, by use of a poller device that provides for scheduling and work distribution to serverless code executions based on data items within a data stream. Moreover, the presently disclosed embodiments address technical problems inherent within computing systems; specifically, the need to maintain state information when conducting data analytics, the difficulty of maintaining such information without increasing computing resources used to process a data stream or decreasing flexibility in where such processing occurs, and the need to orchestrate serverless computing systems to implement streaming analytics over various time windows. These technical problems are addressed by the various technical solutions described herein, including the use of a poller device to orchestrate serverless compute executions to implement streaming analytics, while maintaining state information to facilitate those analytics. Thus, the present disclosure represents an improvement on existing systems and computing systems in general.

FIG. 2 is a block diagram of an illustrative operating environment 100 for a serverless code execution system 110, in which a poller fleet 130 may provide for poller devices 132 that facilitate, on behalf of client devices 102, streaming analytics of data items published to a message stream 172 of a stream data system 170.

By way of illustration, various example client devices 102 are shown in communication with the serverless code execution system 110, including a desktop computer, laptop, and a mobile phone. In general, the client 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 serverless code execution system 110 may provide the user computing 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 source code (e.g., as part of a disk image), invoking the user-provided source code (e.g., submitting a request to execute the source code on the serverless code execution system 110), scheduling event-based code executions or timed code executions, tracking the user-provided source code, and/or viewing other logging or monitoring information related to their requests and/or source code. 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 illustrative environment 100 further includes one or more auxiliary services 106, which can interact with the serverless code execution environment 110 to implement desired functionality on behalf of a user. Auxiliary services 106 can correspond to network-connected computing devices, such as servers, which generate data accessible to the serverless code execution environment 110 or otherwise communicate to the serverless code execution environment 110. For example, the auxiliary services 106 can include web services (e.g., associated with the user computing devices 102, with the serverless code execution system 110, or with third parties), databases, really simple syndication (“RSS”) readers, social networking sites, or any other source of network-accessible service or data source. In some instances, auxiliary services 106 may be invoked by code execution on the serverless code execution system 110, such as by API calls to the auxiliary services 106. In some instances, auxiliary services 106 may be associated with the serverless code execution system 110, e.g., to provide billing or logging services to the serverless code execution system 110. In some instances, auxiliary services 106 actively transmit information, such as API calls or other task-triggering information, to the serverless code execution system 110. In other instances, auxiliary services 106 may be passive, such that data is made available for access by the serverless code execution system 110. For example, components of the serverless code execution system 110 may periodically poll such passive data sources, and trigger execution of code within the serverless code execution system 110 based on the data provided. While depicted in FIG. 2 as distinct from the user computing devices 102 and the serverless code execution system 110, in some embodiments, various auxiliary services 106 may be implemented by either the user computing devices 102 or the serverless code execution system 110.

The illustrative environment 100 further includes a stream data system 170. As discussed above, the stream data processing system can provides the ability for upstream devices to place data onto a message stream 172, such as by publishing “messages” onto the stream 172, which may be designated based on a specific “topic.” While a single stream 172 is shown in FIG. 1, the system 170 may provide multiple streams on behalf of multiple parties. The system 170 can make messages within the stream 172 available to downstream devices, often in a “first-in-first-out” (“FIFO”) or nearly FIFO order. In some instances, the stream data system 170 “pushes” messages to downstream devices. In other instances, downstream devices “pull” messages from the message stream 172 on request. Generally, the stream data system 170 is configured to provide resiliency, such that data successfully published to the stream is unlikely to be lost due to failures of devices of the stream data system 170. For example, the system 170 may duplicate messages placed onto the stream 172 onto multiple computing devices used to implement the stream (e.g., physical computing devices or virtual devices implemented on physical hosts). Moreover, the stream data system 170 can be configured to provide parallelization of the devices that maintain the message stream 172. For example, a user configuring a message stream may designate a partition key for the stream, used to divide the stream into sub-streams, each sub-stream handled by one or more parallelized devices. The sub-streams are shown in FIG. 1 as message shards 174A-N. Each message shard 174 can generally represent one or more computing devices configured to obtain and make available a subset of messages on the message stream, selected by the system 170 according to the partition key and a volume of messages on the stream 170 (e.g., such that additional shards are created, or excess shards are destroyed, based on a capacity of the shards 174 to service messages on the stream 172). In some instances, a stream 172 may contain only a single shard. Examples of stream data processing systems known in the art include the AMAZON™ KINESIS™ network service and the APACHE™ KAFKA™ system.

The client devices 102, auxiliary services 106, stream data system 170, and serverless code execution system 110 may communicate via a network 104, which may include any wired network, wireless network, or combination thereof. For example, the network 104 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 104 may be a publicly accessible network of linked networks, possibly operated by various distinct parties, such as the Internet. In some embodiments, the network 104 may be a private or semi-private network, such as a corporate or university intranet. The network 104 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 104 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 104 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.

The serverless code execution system 110 and stream data system 170 are depicted in FIG. 1 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). Either or both of the serverless code execution system 110 and stream data system 170 could also operate within a computing environment having a fewer or greater number of devices than are illustrated in FIG. 1. Thus, the depiction of the serverless code execution system 110 and stream data system 170 in FIG. 1 should be taken as illustrative and not limiting to the present disclosure. For example, the serverless code execution system 110 and stream data system 170 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 serverless code execution system 110 and stream data system 170 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 data centers. In some instances, the one or more servers may operate as part of a system of rapidly provisioned and released computing resources, often referred to as a “cloud computing environment.”

In the example of FIG. 1, the serverless code execution system 110 and stream data system 170 are illustrated as connected to the network 104. In some embodiments, any of the components within the serverless code execution system 110 and stream data system 170 can communicate with other components of the serverless code execution system 110 and stream data system 170 via the network 104. In other embodiments, another network (such as a private network not shown in FIG. 1) may enable communication between components within each of the serverless code execution system 110 and stream data system 170 or between those systems.

In FIG. 2, users, by way of client computing devices 102, may interact with the serverless code execution system 110 to provide source code, and establish rules or logic defining when and how such code should be executed on the serverless code execution system 110, thus establishing a “task” or “function.” 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 serverless code execution system 110, and request that the serverless code execution system 110 execute the code using one or more execution environments that are managed by the system 110. The serverless code execution system 110 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 serverless code execution system 110 may automatically scale up and down based on the volume of request to execute code, 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 code, and thus overpaying). In accordance with embodiments of the present disclosure, the functions established by a user may correspond to code executable to implement streaming analytics for data items on the data stream 172, including an aggregation function to generate state information for data items within a time window and a destination function to handle a result corresponding to that time window.

To enable interaction with the serverless code execution system 110, the system 110 includes multiple frontends 120, which enable interaction with the serverless code execution system 110. In an illustrative embodiment, the frontends 120 serve as a “front door” to the other services provided by the serverless code execution system 110, enabling users (via user computing devices 102) to provide, request execution of, and view results of computer executable source code. The frontends 120 include a variety of components to enable interaction between the serverless code execution system 110 and other computing devices. For example, each frontend 120 may include a request interface providing user computing devices 102 with the ability to upload or otherwise communication user-specified code and associated data sets to the serverless code execution system 110 (e.g., in the form of a disk image) and to thereafter request execution of that code. In one embodiment, the request interface communicates with external computing devices (e.g., user computing devices 102, auxiliary services 106, etc.) via a graphical user interface (GUI), CLI, or API. The frontends 120 process the requests and makes sure that the requests are properly authorized. For example, the frontends 120 may determine whether the user associated with the request is authorized to access the source code specified in the request.

References to source code as used herein may refer to any program code (e.g., a program, routine, subroutine, thread, etc.) written in a specific program language. In the present disclosure, the terms “source code,” “user code,” and “program code,” may be used interchangeably. Source code which has been compiled for execution on a specific device is generally referred to herein as “machine code.” Both “source code” and “machine code” are representations of the same instructions, which may be collectively referred to as “code.” Such code may be executed to achieve a specific function, for example, in connection with a particular web application or mobile application developed by the user. As noted above, individual collections of code (e.g., to achieve a specific function) are referred to herein as “tasks” or “functions,” while specific executions of that code are referred to as “task executions,” “function executions,” “code executions,” or simply “executions.” Source code for a task 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). Tasks may be “triggered” for execution on the serverless code execution system 110 in a variety of manners. In one embodiment, a user or other computing device may transmit a request to execute a task may, which can generally be referred to as “call” to execute of the task (e.g., a “task call,” a “function call,” etc.). Such calls may include an identifier of the task to be executed and one or more arguments to be used for executing the task. A request interface of the frontend 120 may receive calls to execute tasks as Hypertext Transfer Protocol Secure (HTTPS) 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. As discussed above, any other protocols, including, for example, HTTP, MQTT, and CoAP, may be used to transfer the message containing a task call to the request interface.

Prior to calling for execution of a function, an end user may submit (e.g., to a frontend 120) code for the function and associated data to be used to execute the function. In one embodiment, the code is provided in the form of a disk image containing the code and other data that the code may use during execution. Illustratively, creation of a function may result in the frontend 120 creating metadata for the function, which defines for example the user creating the function, the disk image used to facilitate execution of the function, trigger conditions for the function, and the like. In one embodiment, functions may be versioned, with function metadata identifying the available versions and at least some other metadata for a function may vary across versions. For example, different versions may be associated with different disk images. Function data and metadata is illustratively stored in the function data store 160. The function data store 160 correspond to any persistent data store. In one embodiment, the function data store 160 is implemented as logical storage on a cloud storage service, such as an object storage system. An example of such an object storage system is AMAZON™'s SIMPLE STORAGE SERVICE™ (or “S3™).

In accordance with embodiments of the present disclosure, user-submitted code may correspond to functions for conducting streaming analytics, such as aggregation functions 162 and destination functions 164. The functions may be embodied in computer-executable code submitted to the execution system 110. In one embodiment, the aggregation function 162 implements data analysis, accepting data items from a data stream and state information for a current window (if any), and producing new state information for the window. The specific functionalities of an aggregation function may vary according to the data to be processed and a desired result. However, in general terms, an aggregation function may aggregate data items within a window and provide an aggregate result. For example, an aggregation function may count instances of a field value within data items, provide an average of a numerical field value, provide another statistical measure of matching field values, etc. In accordance with embodiments of the present disclosure, the aggregation function maintains state within a window, such as a fixed or sliding window. A final execution of the aggregation function with respect to a given window provides final state for that window, which may be passed to a destination function 164, representing code executable to handle that final state. For example, the destination function may evaluate the state to determine a result (e.g., whether an alert should or should not be sent), publish the state to a network destination, etc. Thus, the destination function 164 enables a result of streaming analytics to be provided for a given window. While shown as distinct functions, the aggregation and destination functions 162 and 164 may in some instances be merged as a single function. Both functions 162 and 164 may be stored within the function data store 160.

After a user has created a function on the serverless code execution system 110, the system 110 may accept calls to execute that function. To calls to execute a function, the frontend 120 can include an execution queue, which can maintain a record of requested task executions. Illustratively, the number of simultaneous function executions by the serverless code execution system 110 is limited, and as such, new function executions initiated at the serverless code execution system 110 (e.g., via an API call, via a call from an executed or executing function, etc.) may be placed on the execution queue and processed, e.g., in a first-in-first-out order. In some embodiments, the serverless code execution system 110 may include multiple execution queues, such as individual execution queues for each user account. For example, users of the serverless code execution system 110 may desire to limit the rate of function executions on the serverless code execution system 110 (e.g., for cost reasons). Thus, the serverless code execution system 110 may utilize an account-specific execution queue to throttle the rate of simultaneous function executions by a specific user account. In some instances, the serverless code execution system 110 may prioritize function executions, such that function executions of specific accounts or of specified priorities bypass or are prioritized within the execution queue. In other instances, the serverless code execution system 110 may execute functions immediately or substantially immediately after receiving a call for that function, and thus, the execution queue may be omitted.

In addition to functions executed based on explicit user calls and data from auxiliary services 106, the serverless code execution system 110 may in some instances operate to trigger execution of functions independently. For example, the serverless code execution system 110 may operate (based on instructions from a user) to trigger execution of a function at each of a number of specified time intervals (e.g., every 10 minutes).

The frontend 120 can further includes an output interface configured to output information regarding the execution of functions on the serverless code execution system 110. Illustratively, the output interface may transmit data regarding function executions (e.g., results of a function, errors related to the function execution, or details of the function execution, such as total time required to complete the execution, total data processed via the execution, etc.) to the user computing devices 102 or to auxiliary services 106, which may include, for example, billing or logging services. The output interface may further enable transmission of data, such as service calls, to auxiliary services 106. For example, the output interface may be utilized during execution of a function to transmit an API request to an external service 106 (e.g., to store data generated during execution of the function).

Code executions triggered on the serverless code execution system 110 of FIG. 1 are executed by execution environments hosted by a set of workers 181 within a worker fleet 180. Each worker 181 is illustratively a host device configured to host multiple execution environments, which in FIG. 1 are virtual machine instances 183A-N. Execution environments may alternatively include software containers, sometimes referred to as “OS-level virtualization,” another virtualization technology known in the art. Thus, where references are made herein to VM instances 183, it should be understood that (unless indication is made to the contrary) a container may be substituted for such instances 183.

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”). Due to their emulation of hardware, these virtual machine instances are sometimes referred to as “system virtual machines.” 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 emulate 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.

As shown in FIG. 1, each worker 181 may host a number of instances 183. Each instance 183 may be isolated from other instances 183, thus ensuring the security of code executions on the serverless code execution system 110. For example, each instance 183 may be divided by a virtualization boundary, by virtue of the instance 183 being a virtual machine hosted by the worker 181. In addition, each instance 183 may exist within a partitioned user space on the worker 181, which logically partitions resources of the worker 181 among instances 183. Each user space may, for example, represent a “chroot” jail—a known isolation technique for LINUX™ operating systems.

To facilitate rapid execution of code, each worker 181 may be configured to maintain a set of instances 183 in a “pre-warmed” state, being at least partially configured to begin execution of code. For example, instances may be created on the worker and configured with access to computing resources (CPU, RAM, drive storage, etc.). In some cases, it may be impractical or impossible to maintain instances 183 in a fully warmed state for all possible code executions, as executions may be associated with a wide variety of at least partially distinct data sets (e.g., disk images and/or snapshots). Thus, instances 183 may be maintained in a “greatest commonality” for a given group of tasks, such as being provisioned with a set of computing resources common to those tasks, being configured to accept an operating system type used by those tasks, etc.

On receiving instructions to provision an instance 183 to support execution of the task, the worker 181 may adjust the configuration of the instance 183 to support that execution. Specifically, the worker 181 may provision the instance 183 with access to a disk image or snapshot corresponding to the task. In some instances, the worker 181 may retrieve the disk image for the task and store the full image locally. In other instances, the worker 181 may provide to an instance 183 what appears to be full local access to the disk image or snapshot, while “lazily” retrieving portions of that image or snapshot in response to a request to read such portions. Techniques for providing lazy retrieval of image portions are discussed in the U.S. patent application Ser. No. 17/105,250, filed Nov. 25, 2020 and entitled “LOW LATENCY ACCESS TO DATA SETS USING SHARED DATA SET PORTIONS” (the “'250 Application”) the entirety of which is hereby incorporated by reference.

In addition, the system 110 includes a number of components for facilitating distribution of calls to execute a function from frontends 120 to particular VM instances 183. For example, the serverless code execution system 110 includes one or more worker managers 140 configured to manage execution environments (e.g., virtual machine instances) hosted by workers 181 among a worker fleet 180. The worker managers 140—each of which are illustratively implemented as physical or virtual-on-physical devices—illustratively “lease” particular VM instances 183 within the fleet 180, thus gaining operational control to, for example, instruct virtual machine instances 183 to execute code of the function. Thus, on receiving a call to execute a function, a frontend 120 may distribute the call to a worker manager 140, which may identify a currently-leased VM instance 183 in which to implement the function, and cause the instance 183 to implement the function.

In the instance that a worker manager 140 does not currently lease a VM instance 183 corresponding to the called function, the worker manager 140 can contact a placement service 160 to request a lease on an additional instance 183, which is illustratively configured to grant to the worker managers 140 leases to individual VM instances 183. Illustratively, the placement service 160 may maintain state information for VM instances 183 across the fleet 180, as well as information indicating which manager 140 has leased a given instance 183. When a worker manager 140 requests a lease on an additional instance 183, the placement service 160 can identify an appropriate instance 183 (e.g., warmed with software and/or data required to support a call to implement a function) and grant to the manager 140 a lease to that instance 183. In the case that such an instance 183 does not exist, the placement service 160 can instruct a worker 181 to create such an instance 183 (e.g., by creating an instance 183 or identifying an existing unused instance 183, providing the instance 183 with access to a required set of data to support execution, etc.) thereafter grant to the worker manager 140 a lease to that instance 183, thus facilitating execution.

To facilitate interaction with external data sources, such as the stream data system 170 or auxiliary services 106, the system 110 including a polling fleet 130, which operates to poll external data sources for data. Illustratively, the polling fleet 130 may include one or more computing devices (shown in FIG. 1 as poller devices 132A-N) configured to periodically transmit a request to the stream data system 170 to retrieve any newly available data (e.g., social network “posts,” news articles, files, records, etc.), and to determine whether that data corresponds to a user-established criteria triggering execution a function on the serverless code execution system 110. Illustratively, criteria for execution of a function may include, but is not limited to, whether new data is available at the auxiliary services 106 or the stream data system 170, the type or content of the data, or timing information corresponding to the data. In some instances, the auxiliary services 106 or stream data system 170 may function to notify the frontend 120 of the availability of new data, and thus the polling fleet 130 may be unnecessary with respect to such services.

In accordance with embodiments of the present disclosure, the poller fleet 130 can be configured to include a dynamic number of poller devices 132A-N (e.g., implemented as virtual machine instances on an underlying computing system), based on the number of message shards 174 within a message stream 172. For example, as shown by the dotted lines of FIG. 1, message shard 174A may correspond to poller device 132A, message shard 174B may correspond to poller device 132B, etc. Thus, as the number of message shards 174 changes (e.g., due to volume of the message stream), the number of poller devices 132 may also change. As such, the poller fleet 130 may be in communication with stream data system 170, and the system 170 may notify the poller fleet 130 of changes to the message shards 174. In such a configuration, each poller device 132A can be configured to poll a message shard 174 to retrieve messages in the sub-stream corresponding to the message shard. The messages may be retrieved individually or in batches (e.g., batches of 10 messages, 50 messages, 100 messages, 500 messages, etc.). Thereafter, the poller device 132 may invoke calls to aggregation functions 162 or destination functions 164 as appropriate for the messages. In some instances, the call from each poller device 132 to corresponding function executions may be made synchronously, such that the poller device 132 waits for confirmation that the execution was successful prior to making a next call.

While some functionalities are generally described herein with reference to an individual component of the serverless code execution system 110 or the stream data system 170, other components or a combination of components may additionally or alternatively implement such functionalities. For example, while a poller device 132A may operate to poll a message shard 174 for messages, the message shards 174 may additionally or alternatively be configured to notify the serverless code execution system 110 (e.g., the frontend) of new messages on the shard 174.

FIG. 3 depicts a general architecture of a poller device 132. The general architecture of the poller device 132 depicted in FIG. 3 includes an arrangement of computer hardware and software modules that may be used to implement aspects of the present disclosure. The hardware modules may be implemented with physical electronic devices, as discussed in greater detail below. The poller device 132 may include many more (or fewer) elements than those shown in FIG. 3. It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. Additionally, the general architecture illustrated in FIG. 3 may be used to implement one or more of the other components illustrated in FIG. 1. As illustrated, the poller device 132 includes a processing unit 190, a network interface 192, a computer readable medium drive 194, and an input/output device interface 196, all of which may communicate with one another by way of a communication bus. The network interface 192 may provide connectivity to one or more networks or computing systems. The processing unit 190 may thus receive information and instructions from other computing systems or services via the network 104. The processing unit 190 may also communicate to and from memory 180 and further provide output information for an optional display (not shown) via the input/output device interface 196. The input/output device interface 196 may also accept input from an optional input device (not shown).

The memory 180 may contain computer program instructions (grouped as modules in some embodiments) that the processing unit 190 executes in order to implement one or more aspects of the present disclosure. The memory 180 generally includes random access memory (RAM), read only memory (ROM) and/or other persistent, auxiliary or non-transitory computer readable media. The memory 180 may store an operating system 184 that provides computer program instructions for use by the processing unit 190 in the general administration and operation of the worker manager 140. The memory 180 may further include computer program instructions and other information for implementing aspects of the present disclosure. For example, in one embodiment, the memory 180 includes a user interface unit 182 that generates user interfaces (and/or instructions therefor) for display upon a computing device, e.g., via a navigation and/or browsing interface such as a browser or application installed on the computing device. In addition, the memory 180 may include and/or communicate with one or more data repositories (not shown), for example, to access user program codes and/or libraries.

In addition to and/or in combination with the user interface unit 182, the memory 180 may include a polling unit 186, data analysis unit 188, and serverless interface unit 189. In one embodiment, the polling unit 186, data analysis unit 188, and serverless interface unit 189 individually or collectively implement various aspects of the present disclosure. For example, the polling unit 186 can represent code executable to poll a message stream 172 to identify and obtain data items from the stream 172. The data analysis unit 188 can represent code executable to analyze those data items to determine whether criteria are satisfied for invoking an aggregation or destination function associated with the stream. The serverless interface unit 189 can represent code executable to invoke such aggregation or destination functions, and to maintain state information between such invocations.

While the polling unit 186, data analysis unit 188, and serverless interface unit 189 are shown in FIG. 3 as part of the poller device 132, in other embodiments, all or a portion of the polling unit 186, data analysis unit 188, and serverless interface unit 189 may be implemented by other components of the serverless code execution system 110 and/or another computing device. For example, in certain embodiments of the present disclosure, another computing device in communication with the serverless code execution system 110 may include several modules or components that operate similarly to the modules and components illustrated as part of the poller device 132.

With reference to FIG. 4, illustrative interactions are depicted for initiating streaming analysis on data items in a message stream 172 using the serverless code execution system 110 of FIG. 2. Specifically, the interactions of FIG. 4 are illustrative of those that may be undertaken by the system 110 to receive and respond to a user request to conduct streaming analytics according to aggregation and destination functions provided by the user.

The interactions of FIG. 4 begin at (1), where a client device 102 configures the serverless code execution system 110 to implement streaming analytics by configuring aggregation and destination functions on the system 110. In the illustrative interactions of FIG. 3, the aggregation and destination functions are designated as serverless functions within the serverless code execution system 110, which may have been previously created by the user device 102 or which may be created by the serverless code execution system 110 as part of configuring streaming analytics (e.g., the user may submit code for the aggregation and destination functions as part of configuring those functions to implement streaming analytics). In other embodiments, a user may designate other aggregation and destination functions, such as functions made available by the serverless code execution system 110 or other users. In addition to designation of aggregation and destination functions, the illustrative configuration of FIG. 4 generally includes specification of a data stream (e.g., the message stream 172 of FIG. 1) including data items (or “messages”) to be processed via the aggregation and destination functions, as well as criteria for invoking the aggregation and destination functions. Such criteria can include windowing criteria specifying windows over which messages should be analyzed (e.g., sliding and/or fixed windows), including for example window durations or criteria for establishing such durations. The destination function may illustratively be executed at the end of each such window, using state information associated with processing the data items occurring within the window. The criteria for invoking the aggregation and destination functions can further include aggregation criteria, specifying when the aggregation function should be run to process data items within the window. For example, the aggregation criteria may include a maximum number or data size of items to be processed by an individual invocation of the aggregation function. As discussed below, the criteria for invoking the aggregation and destination functions can thereafter be applied by poller devices 132 within the poller fleet 132 to conduct streaming analytics against a message stream 172 by invocation of the aggregation and destination functions.

Accordingly, the frontend 120, at (2), transmits the provided aggregation and destination functions (if required) to the task data store 160, for later retrieval and execution. Additionally, at (3), the frontend 120 instructs the poller fleet to initialize streaming analytics as specified by the client device 102. The frontend 120 may illustratively pass to the poller fleet identification of a message stream 172 containing data to be analyzed, identification of the aggregation and destination functions, and the criteria for invoking the aggregation and destination functions. The poller fleet 130 then, at (4), initializes poller devices 132 in order to conduct streaming analytics. Illustratively, the poller fleet 130 may initialize one or more poller devices for each shard 174 of the message stream 172.

With reference to FIG. 5, illustrative interactions are shown for conducting streaming analytics of messages within a message stream 172 by invocation of an aggregation function on the serverless code executions system 110. For example, the interactions of FIG. 5 may occur in response the interactions of FIG. 4, described above.

The interactions of FIG. 5 begin at (1), where one or more messages are published to a messages stream 172 on the stream data system 170. The messages may be published by and number of data sources, such as client devices 102, auxiliary services 106, or other network devices. For example, the messages may be published during operation of a computing system, in order to log to the stream 172 data regarding operation of the computing system. The messages may contain any of a wide variety of types of data, corresponding to data analyzed via execution of aggregation and destination functions.

At (2), the poller fleet 130 (e.g., using poller devices 132) retrieves messages from the stream 172. In one embodiment, retrieval utilizes a “pull” mechanism, whereby the fleet 130 periodically (e.g., every second, 10 seconds, 30 seconds, etc.) pulls new messages from the stream 172. In another embodiment, retrieval uses a “push” mechanism, whereby the stream 172 notifies the fleet 130 of new messages.

At (3), the poller fleet 130 assigns the retrieved messages to one or more windows, according to windowing criteria. For example, a timestamp associated with each message can be used to assign the message to respective windows. In the case of fixed, non-overlapping windows, each message may be assigned to a single window. In the case of sliding or otherwise overlapping windows, each message may be assigned to multiple windows. For example, each message may provoke creation of a new sliding window of a given duration.

Thereafter, at (4), the poller fleet 130 determines that the retrieved messages for a given window satisfy criteria for invocation of the aggregation function with respect to those messages. Such criteria may include, for example, a number of messages or a total data size of messages. Such criteria may further include a closing of the window including the messages, which may be determined for example based on the presence of messages within the stream with a timestamp subsequent to a closing time for the window.

Accordingly, at (5), the poller fleet 130 invokes the aggregation function to process the messages. In one embodiment, the invocation passes the messages to the aggregation function execution 402. In another embodiment, the invocation identifies the messages on the message stream 172, such that the execution 402 can obtain the messages during execution. In the invocation, the poller fleet 130 additionally passes to the aggregation function state information for the window to which the messages have been assigned. Illustratively, the poller fleet 130, during a first invocation of the aggregation function with respect to a given window, may pass initial state information, which may be null. During subsequent invocations, the aggregation function may be passed updated state information for the window, as described below. The invocation may illustratively be a synchronous execution, such that operation of the fleet 130 or a particular poller device 132 pauses and awaits completion of the execution before proceeding with additional operations.

At (6), the serverless code execution system 110 initiates an aggregation function execution 402. The execution 402 illustratively represents execution of code that analyzes the messages corresponding to the invocation using the passed in state information, if any. For example, the execution 402 may determine a count, average, minimum, or maximum of one or more field values in each message for a given window. One skilled in the art will appreciate that these functionalities are provided for illustration only, and that the aggregation function, in being user-defined, may implement any number of functionalities.

At (7), as a result of processing messages corresponding to the invocation, the aggregation function execution 402 returns to the poller fleet 130 a result as state information for a corresponding window. For example, the execution 402 may pass a count, average, minimum, or maximum value identified during processing of messages for the window to the poller fleet 130. At (8), the fleet updates the state information for the corresponding window with the returned result. Thus, future invocations of the aggregation function can be invoked using this state information, enabling such executions to be stateful and without requiring such state to be maintained within an execution environment of the aggregation function execution 402.

While a single sequence of interactions is shown in FIG. 5, one skilled in the art will appreciate that these interactions may occur multiple times, with some interactions potentially occurring concurrently. For example, messages may be published to the stream independently of operation of the system 110. Similarly, messages may be retrieved from the stream independently of remaining interactions of FIG. 5, and, e.g., cached at the poller fleet 130 for analysis according to streaming analytics criteria. Moreover, interactions (3)-(8) may occur repeatedly with respect to messages in a given window, such that multiple aggregation function executions 402 occur within that window. Similarly, these interactions may be repeated for each window of messages. While the interactions of FIG. 5 are described with reference to the poller fleet 130 generally, these interactions may be duplicated among poller devices 132. For example, each device 132 may be configured to undertake interactions (3)-(8) with respect to a different shard 174 of the stream 172. In some embodiments, multiple devices 132 may be configured to undertake interactions (3)-(8) with respect to a single shard 174. For example, windowing criteria may include partitioning criteria, such as an attribute of messages within a shard 174 to use as a partition key in order to divide the messages (e.g., according to a consistent hash algorithm), with windowing and aggregation criteria. Each poller device 132 of the multiple devices 132 may thereafter apply windowing and aggregation criteria to their respective portion of the messages to implement the interactions noted above.

With reference to FIG. 6, illustrative interactions are shown for conducting streaming analytics of messages within a message stream 172 by invocation of a destination function on the serverless code executions system 110. The interactions may illustratively occur subsequently or concurrently with interactions of FIG. 5. For example, as shown in FIG. 6, the interactions begin at (1), where messages are published to a stream 172, and continue at (2), where the poller fleet 130 retrieves one or more messages from the stream. These interactions are substantially similar to interactions (1) and (2) of FIG. 5, and thus will not be re-described in detail. In some cases, interactions (1) and (2) of FIGS. 5 and 6, respectively, may represent the same interactions. That is, a given set of messages published to the stream 172 and retrieved by the poller fleet 130 may result in the interactions of both FIGS. 5 and 6.

After retrieving messages, at (3), the poller fleet 130 detects a window close. As discussed above, each window can be associated with given start and end periods. As such, detecting a window close can correspond to detecting that the window's end period has occurred. In one embodiment, detecting a window close corresponds to detecting that a message in the stream has a time stamp after the window's end period. This may indicate, for example, that all messages within the window have been published to the stream 172, and are therefore available to the poller fleet 130. In instances where the stream 172 does not guarantee ordering (e.g., where a message with an earlier timestamp is not guaranteed to exist in the stream prior to a message with a later timestamp), the poller fleet 130 may consider unordered messages as part of a later window. For example, any message with a timestamp corresponding to a closed window may be considered by the fleet 130 as part of an earliest open window. In other embodiments, the poller fleet 130 may be configured to attempt to place out-of-order messages into a correct window. For example, the poller fleet 130 may be configured to consider an out-of-order message as included within its appropriate window (according to the timestamp on the message), so long as a destination function for that window has not been invoked. The poller fleet 130 may in some cases be configured to delay invocation of a destination function for each window to account for out of order messages. For example, on detecting a window close, the poller fleet 130 may delay invocation of the destination function for a given period (e.g., 1 second, 10 seconds, 30 seconds, etc.), such that out of order messages obtained during that period can be processed as part of the closed window.

Thereafter, at (4), the poller fleet 130 invokes the destination function with the final state for the window, corresponding to state returned by an execution of the aggregation function after processing the messages corresponding to the window. In one embodiment, the poller fleet 130 is configured to confirm that all messages within a window have been processed by execution of the aggregation function prior to invoking the destination function. If messages exist that have not been processed, the poller fleet 130 may invoke the aggregation function (e.g., in the manner described with respect to FIG. 6) on window close in order to obtain final state for the window. The poller fleet 130 can then invoke the destination function and pass that function the final window state. In response to the invocation, the serverless code execution system 110 initiates a destination function execution 602, which at (5) executes to process the final window state. For example, the destination function execution 602 may evaluate the state to determine an action to be taken (if any), and undertake the relevant action. Relevant actions may include, for example, logging the final state, sending an alert if the final state matches given criteria, etc. Because the evaluation and relevant action are defined within the user-defined destination function, these may encompass a wide variety of functionalities.

At (6), the destination function execution 602 returns to the poller fleet an indication of success. The poller fleet 130 then, at (7), marks the window as processed. Accordingly, because the aggregation and destination functions have been invoked for each message within the window, the requested streaming analytics have been applied to the window. In accordance with the interactions above, the streaming analytics have been conducted without requiring deployment of specific resources to conduct such analytics, enabling end users to enjoy the benefits associated with serverless computing during such analytics. Moreover, such analytics are enabled to operate statelessly, without requiring such state to be maintained in execution environments of the serverless code execution system 110, and thus without inhibiting flexibility of that system 110 in executing user-defined code.

Various modifications may be made to the interactions of FIGS. 5 and 6. For example, while FIGS. 5 and 6 discuss separate aggregation and destination functions, as noted above these functions may in some instances represent a single function. The invocations of FIGS. 5 and 6 may therefore refer to invocation of the same function with, e.g., a flag or other input designating which functionality (aggregation or destination) is to be invoked. In some cases, a single invocation may be used to invoke both aggregation and destination functionality. For example, on detecting a window has closed, the poller fleet may invoke a single function to both process remaining (unprocessed) messages for the window, and use a result of such processing as a final state to implement destination processing. Accordingly, interaction (5) of FIG. 5 and (4) of FIG. 6 may be combined into a single invocation, and interactions (7) and (8) of FIG. 5 may be omitted.

While not shown in FIGS. 5 and 6, the poller fleet 130 may in some embodiments undertake additional interactions to ensure resiliency of operation. For example, each device 132 may periodically “checkpoint” its state to an external storage system, such as auxiliary services 106. Checkpointing may indicate, for example, messages of a stream 172 processed by the aggregation function and state associated with that processing, whether a destination function has been successfully invoked for a window, and the like. In this manner, should a poller device 132, fail, a new poller device 132 may be initialized and enabled to resume operation of the failed poller device 132 using checkpoint information of that failed poller device 132.

Still further, while the interactions of FIGS. 5 and 6 generally contemplate processing for a single continuous set of messages (e.g., within a given stream 172 or shard 174) in some instances streams and/or shards may be split or merged. For example, the stream data system 170 may be configured to split or merge streams on request by users owning such streams, or to split or merge shards according to a volume of message on those shards. The poller fleet 130 may in some embodiments be configured to handle such splits or merges. For example, the poller fleet 130 in detecting a split or a stream or shard may apply the streaming analytics criteria for the unsplit stream or shard to both resulting streams or shards. A user configuring streaming analytics may specify which of two sets of criteria (if different) should be applied to merged streams or shards, such that the poller fleet 130 applies the specified criteria to a merged stream or shard. In one embodiment, the poller fleet 130 treats splits or merges as a window boundary, such that no state information is maintained across splits or merges. In another embodiment, the poller fleet 130 can maintain windows across split/merge boundaries, and handle state information accordingly. For example, in the case of a split, the fleet 130 may duplicate state information of the unsplit shard or stream to both resultant shards or streams. In such an example, an aggregation and/or destination function may include code executable to determine relevant state information for the corresponding shard or stream. In the case of a merger, the fleet 130 may combine or concatenate state information for the merged streams or shards. Various additional modifications to the interactions of FIGS. 5 and 6 may be made.

With reference to FIG. 7, an illustrative routine 700 will be described for conducting streaming analytics using a serverless code execution system. The routine 700 may be implemented, for example, on a poller device 132 of a poller fleet 130.

The routine 700 begins at block 702, where the poller device 132 obtains streaming analytics parameters, including designations of aggregation and destination functions to be used to conduct streaming analytics. The streaming analytics parameters further illustratively include windowing criteria specifying criteria for identifying windows over which to conduct analytics and to invoke the destination function, and aggregation criteria specifying when to invoke an aggregation function to produce state information for the window to be passed to either to a subsequent aggregation function or to the destination function.

Thereafter, the poller device 132 enters the window loop 710 and the aggregation loop 720, as shown in FIG. 7. The window loop 710 illustratively denotes operations taken with respect to a given window in a data stream, such that, e.g., each interaction of the loop occurs with respect to a different window of messages on the stream. Aggregation loop 720 denotes operations taken with respect to subsets of messages within a window, to conduct intermediate processing of those messages and facilitate generation of state information to be used during a subsequent instance of the loop 720, if any, or to be passed to a destination function at the end of the window loop 710.

Within the loops 710 and 720, the poller device 132 obtains messages from the stream. Illustratively, the poller device 132 may obtain messages by reading the messages from the stream. Alternatively, the poller device 132 may include a separate process to read messages from the stream and place them in a local cache of the device 132, from which they may be read during implementation of the routine 700.

At block 706, the poller device 132 assigns each message to a window. Illustratively, the device 132 may inspect an attribute of each message, such as a timestamp, to identify one or more windows corresponding to the message. In the instance of non-overlapping windows, the device may calculate a single window for each message based on window boundaries calculated from a fixed point in time. For example, a starting time (t=0) may be a first boundary, with additional boundaries created at fixed intervals correspond to a duration of each window. In the instance of sliding windows, the poller device 132 may assign each message to a new window with a start time corresponding to the attribute of the message, as well as to any prior windows that include the timestamp of the message and that have not yet closed.

At block 708, the poller device 132 determines whether the messages satisfy aggregation criteria for any open windows. For example, the poller device 132 can determine, for each open window, whether the set of unprocessed messages for the window collectively satisfy aggregation criteria for the window. Aggregation criteria may be satisfied, for example, based on a total number of unprocessed messages, a total size of unprocessed messages, or detecting that the window should be closed (e.g., based on detecting a message with a timestamp attribute subsequent to a close time of the window). If aggregation criteria are not satisfied, the routine 700 returns to block 704, where additional messages are obtained and the routine 700 proceeds as noted above.

When aggregation criteria are satisfied, the routine 700 proceeds to block 712, where the aggregation function is invoked for those unprocessed messages of the stream that satisfied the aggregation criteria. When invoking the aggregation function, the poller device 132 can pass to the aggregation function prior state information for the window, if any. Prior state information may include, for example, an initial state value (e.g., null) or state information returned as a result of a prior invocation of the aggregation function. As discussed above, the aggregation function may then be executed on the serverless compute system 110 using the prior state information, and execution of the aggregation function illustratively returns a result to the poller device 132. The poller device 132, in turn, updates the state for the window corresponding to the invocation at block 714.

The routine 700 then proceeds to block 716, where the poller device 132 determines whether closing criteria is met for any open windows, and whether all messages for that window have been processed by the aggregation function. As noted above, each window may be associate with a timespan on the stream, and closing criteria can thus indicate that the window is to be closed after that timespan has elapsed. For example, the poller device 132 may determine that a window should be closed after a message is detected with a timestamp subsequent to the windows' timespan, a threshold period after such a message is detected, etc. If the closing criteria is not met, or messages remain unprocessed within the window, the routine 700 returns to block 704 and proceeds as noted above. If a window is to be closed and all messages have been processed, the routine 700 exits the aggregation loop 720 with respect to that window and proceeds to block 718, where the poller device 132 invokes the destination function using the final state for the window (e.g., generated based on a invocations of the aggregation function with respect to messages in the window). As noted above, the destination function illustratively processes the final state for the window in order to determine an action, if any, to take with respect to that state, such as reporting the state to an end user, to a logging endpoint, etc. The routine 700 then exists the window loop 710 and returns to block 704, where additional messages of other windows are obtained and processed in the manner above. The routine 700 can then proceed to process the additional messages, thus providing streaming analytics for messages within the data stream.

While FIG. 7 depicts one example routine 700, various additions or modifications to the routine 700 may be made. For example, as noted above, a poller device 132 may in some instance implement checkpointing or other functionality to provide resiliency of operation, such as by logging state of the poller device 132 at various times. As another example, while invocation of aggregation and destination functions is discussed separately, in some instances these invocations may be combined. For example, the routine 700 may be modified to combine blocks 712 and 718 in cases where the aggregation function is being invoked due to window closure. A receiving function (e.g., representing a combination of an aggregation and destination function) may then process any unprocessed messages to generate final state, and implement destination functionality based on that final state. This may obviate need for block 714 with respect to final invocations of the aggregation function in cases where that function is invoked due to window closure. Various additional modifications may be made.

All of the methods and processes described above may be embodied in, and fully automated via, software code modules executed by one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods may alternatively be embodied in specialized computer hardware.

Conditional language such as, among others, “can,” “could,” “might” or “may,” unless specifically stated otherwise, are otherwise understood within the context as used in general to present that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/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 user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.

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 and/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 or at least one of Z to each be present.

Unless otherwise explicitly stated, articles such as ‘a’ or ‘an’ should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.

Any routine descriptions, elements or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or elements in the routine. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, or executed out of order from that shown or discussed, including substantially synchronously or in reverse order, depending on the functionality involved as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.

Claims

1. A system for implementing streaming analytics using serverless code executions, the system comprising: a streaming data system comprising a set of computing devices configured to host a data stream comprising messages, individual messages within the data stream being associated with timestamps corresponding to relative positions within the data stream;a serverless computing system configured to obtain invocations of serverless functions and in response initiate execution of the serverless functions, wherein the serverless functions comprise: an aggregation function representing code executable to process one or more input messages from the data stream and generate state information representing analysis of the one or more input messages; anda destination function representing code executable to process state information from the aggregation function; anda poller device configured to: iteratively retrieve messages from the data stream;using window criteria, assign each retrieved message to a window from a plurality of windows; andfor each window of the plurality of windows: group messages assigned to the window according to aggregation criteria to result in at least a first group of messages and a second group of messages;invoke a first execution of the aggregation function to process the first group of messages;obtain first state information from the first execution;invoke a second execution of the aggregation function to process the second group of messages at least partly by passing the first state information to the second execution, wherein the second execution of the aggregation function results in second state information; andinvoke an execution of the destination function at least partly by passing to the execution of the destination function the second state information, wherein execution of the destination function provides processing of a result of conducting the streaming analytics against the messages assigned to the window.
2. The system of claim 1, wherein the aggregation function represents code executable to provide at least one of a count, an average value, a maximum value, or a minimum value of a field within the one or more input messages.
3. The system of claim 1, wherein the windowing criteria specify non-overlapping windows of a fixed length.
4. The system of claim 1, wherein the windowing criteria specify sliding windows, and wherein the poller device is further configured to add a new window to the plurality of windows for each message retrieved from the data stream.
5. The system of claim 1, wherein the data stream is divided into a plurality of shards, and wherein the poller device is included within a plurality of poller devices comprising at least one poller device assigned to each shard of the plurality of shards, and wherein iteratively retrieving messages from the data stream comprises iteratively retrieving messages from the shard to which the poller device is assigned.
6. A computer-implemented method comprising: iteratively retrieving messages from a data stream;using window criteria, assigning each retrieved message to a window from a plurality of windows; andfor each window of the plurality of windows:grouping messages assigned to the window according to aggregation criteria to result in at least a first group of messages and a second group of messages;invoking a first execution of an aggregation function on a serverless computing system, wherein the aggregation function represents code executable to process an input group of messages and provide state information as a result, wherein the first group of messages represents the input group for the first execution, and wherein the result of the first execution is first state information;obtaining the first state information from the first execution;invoking a second execution of the aggregation function on the serverless computing system, wherein the second group of messages represents the input group for the second execution, wherein invoking the second execution comprises passing the first state information to the second execution, and wherein the result of the second execution is second state information; andinvoking an execution of a destination function on the serverless compute system, wherein the destination function represents code executable to process input state information from the aggregation function and provide a result, and wherein the second state information represents the input state information for the execution of the destination function.
7. The computer-implemented method of claim 6, wherein the aggregation function and destination function represent a single function on the serverless compute system, wherein invoking the aggregation function comprises invoking the single function with an input requesting functionality of the aggregation function, and wherein invoking the destination function comprises invoking the single function with an input requesting functionality of the destination function.
8. The computer-implemented method of claim 6, wherein invoking the aggregation function comprises passing existing state information for a window associated with invocation of the aggregation function, and wherein the method further comprises generating initial state information for each window of the plurality of windows.
9. The computer-implemented method of claim 6, wherein passing the first state information to the second execution comprises passing the first state information as a parameter during invocation of the second execution.
10. The computer-implemented method of claim 6 further comprising, prior to invoking the execution of the destination function, determining that the second state information represents final state information for a current window at least partly by determining that no additional messages assigned to the window are awaiting processing by the aggregation function and determining that a timespan of the current window has elapsed.
11. The computer-implemented method of claim 10, wherein determining that the timespan of the current window has elapsed comprises determining that a retrieved message is associated with a timestamp subsequent to the timespan.
12. The computer-implemented method of claim 6, wherein the aggregation criteria specify at least one of a maximum number of messages within each group or a maximum data size of messages within each group.
13. The computer-implemented method of claim 6, wherein the windowing criteria specify sliding windows, and wherein the method further comprises adding a new window to the plurality of windows for each message retrieved from the data stream.
14. Non-transitory computer-readable media comprising instructions that, when executed by a computing system, causes the computing system to: iteratively retrieve messages from a data stream;using window criteria, assign each retrieved message to a window from a plurality of windows; andfor each window of the plurality of windows:group messages assigned to the window according to aggregation criteria to result in at least a first group of messages and a second group of messages;invoke a first execution of an aggregation function on a serverless computing system, wherein the aggregation function represents code executable to process an input group of messages and provide state information as a result, wherein the first group of messages represents the input group for the first execution, and wherein the result of the first execution is first state information;invoke a second execution of the aggregation function on the serverless computing system, wherein the second group of messages represents the input group for the second execution, wherein invoking the second execution comprises passing the first state information to the second execution, and wherein the result of the second execution is second state information; andinvoke an execution of a destination function on the serverless compute system, wherein the destination function represents code executable to process input state information from the aggregation function and provide a result, and wherein the second stale information represents the input stale information for the execution of the destination function.
15. The non-transitory computer-readable media of claim 14, wherein the aggregation function and destination function represent a single function on the serverless compute system, wherein the instructions, when executed, cause the computing system to invoke the aggregation function at least partly by invoking the single function with an input requesting functionality of the aggregation function, and wherein the instructions, when executed, cause the computing system to invoke the destination function at least partly by invoking the single function with an input requesting functionality of the destination function.
16. The non-transitory computer-readable media of claim 14, wherein the instructions, when executed, cause the computing system to invoke the aggregation function at least partly by passing existing state information for a current window to the aggregation function, and wherein the instructions, when executed, cause the computing system to generate initial state information for each window of the plurality of windows.
17. The non-transitory computer-readable media of claim 14, wherein to pass the first state information to the second execution, the instructions, when executed, cause the computing system to pass the first state information as a parameter during invocation of the second execution.
18. The non-transitory computer-readable media of claim 14, wherein the instructions, when executed, further cause the computing system to, prior to invocation of the execution of the destination function, determine that the second state information represents final state information for a current window at least partly by determining that no additional messages assigned to the window are awaiting processing by the aggregation function and determining that a timespan of the current window has elapsed.
19. The non-transitory computer-readable media of claim 18, wherein determining that the timespan of the current window has elapsed comprises determining that a retrieved message is associated with a timestamp subsequent to the timespan.
20. The non-transitory computer-readable media of claim 18, wherein the windowing criteria specify sliding windows, and wherein the instructions, when executed, further cause the computing system to add a new window to the plurality of windows for each message retrieved from the data stream.

US Referenced Citations (850)

Number	Name	Date	Kind
4949254	Shorter	Aug 1990	A
5283888	Dao et al.	Feb 1994	A
5835764	Platt et al.	Nov 1998	A
5970488	Crowe et al.	Oct 1999	A
5983197	Enta	Nov 1999	A
6237005	Griffin	May 2001	B1
6260058	Hoenninger et al.	Jul 2001	B1
6385636	Suzuki	May 2002	B1
6463509	Teoman et al.	Oct 2002	B1
6501736	Smolik et al.	Dec 2002	B1
6523035	Fleming et al.	Feb 2003	B1
6549936	Hirabayashi	Apr 2003	B1
6708276	Yarsa et al.	Mar 2004	B1
7036121	Casabona et al.	Apr 2006	B1
7308463	Taulbee et al.	Dec 2007	B2
7340522	Basu et al.	Mar 2008	B1
7360215	Kraiss et al.	Apr 2008	B2
7558719	Donlin	Jul 2009	B1
7577722	Khandekar et al.	Aug 2009	B1
7590806	Harris et al.	Sep 2009	B2
7665090	Tormasov et al.	Feb 2010	B1
7707579	Rodriguez	Apr 2010	B2
7730464	Trowbridge	Jun 2010	B2
7774191	Berkowitz et al.	Aug 2010	B2
7823186	Pouliot	Oct 2010	B2
7831464	Nichols et al.	Nov 2010	B1
7870153	Croft et al.	Jan 2011	B2
7886021	Scheifler et al.	Feb 2011	B2
7949677	Croft et al.	May 2011	B2
7954150	Croft et al.	May 2011	B2
8010679	Low et al.	Aug 2011	B2
8010990	Ferguson et al.	Aug 2011	B2
8024564	Bassani et al.	Sep 2011	B2
8046765	Cherkasova et al.	Oct 2011	B2
8051180	Mazzaferri et al.	Nov 2011	B2
8051266	DeVal et al.	Nov 2011	B2
8065676	Sahai et al.	Nov 2011	B1
8065682	Baryshnikov et al.	Nov 2011	B2
8095931	Chen et al.	Jan 2012	B1
8127284	Meijer et al.	Feb 2012	B2
8146073	Sinha	Mar 2012	B2
8166304	Murase et al.	Apr 2012	B2
8171473	Lavin	May 2012	B2
8201026	Bornstein et al.	Jun 2012	B1
8209695	Pruyne et al.	Jun 2012	B1
8219987	Vlaovic et al.	Jul 2012	B1
8296267	Cahill et al.	Oct 2012	B2
8321554	Dickinson	Nov 2012	B2
8321558	Sirota et al.	Nov 2012	B1
8336079	Budko et al.	Dec 2012	B2
8352608	Keagy et al.	Jan 2013	B1
8387075	McCann et al.	Feb 2013	B1
8392558	Ahuja et al.	Mar 2013	B1
8402514	Thompson et al.	Mar 2013	B1
8417723	Lissack et al.	Apr 2013	B1
8429282	Ahuja	Apr 2013	B1
8448165	Conover	May 2013	B1
8479195	Adams et al.	Jul 2013	B2
8490088	Tang	Jul 2013	B2
8555281	Van Dijk et al.	Oct 2013	B1
8560699	Theimer et al.	Oct 2013	B1
8566835	Wang et al.	Oct 2013	B2
8601323	Tsantilis	Dec 2013	B2
8613070	Borzycki et al.	Dec 2013	B1
8615589	Adogla et al.	Dec 2013	B1
8631130	Jackson	Jan 2014	B2
8667471	Wintergerst et al.	Mar 2014	B2
8677359	Cavage et al.	Mar 2014	B1
8694996	Cawlfield et al.	Apr 2014	B2
8700768	Benari	Apr 2014	B2
8719415	Sirota et al.	May 2014	B1
8725702	Raman et al.	May 2014	B1
8756322	Lynch	Jun 2014	B1
8756696	Miller	Jun 2014	B1
8763091	Singh et al.	Jun 2014	B1
8769519	Leitman et al.	Jul 2014	B2
8793676	Quinn et al.	Jul 2014	B2
8799236	Azari et al.	Aug 2014	B1
8799879	Wright et al.	Aug 2014	B2
8806468	Meijer et al.	Aug 2014	B2
8806644	McCorkendale et al.	Aug 2014	B1
8819679	Agarwal et al.	Aug 2014	B2
8825863	Hansson et al.	Sep 2014	B2
8825964	Sopka et al.	Sep 2014	B1
8839035	Dimitrovich et al.	Sep 2014	B1
8850432	Mcgrath et al.	Sep 2014	B2
8869300	Singh et al.	Oct 2014	B2
8874952	Tameshige et al.	Oct 2014	B2
8904008	Calder et al.	Dec 2014	B2
8966495	Kulkarni	Feb 2015	B2
8972980	Banga et al.	Mar 2015	B2
8990807	Wu et al.	Mar 2015	B2
8997093	Dimitrov	Mar 2015	B2
9002871	Bulkowski et al.	Apr 2015	B2
9021501	Li et al.	Apr 2015	B2
9027087	Ishaya et al.	May 2015	B2
9038068	Engle et al.	May 2015	B2
9052935	Rajaa	Jun 2015	B1
9086897	Oh et al.	Jul 2015	B2
9086924	Barsness et al.	Jul 2015	B2
9092837	Bala et al.	Jul 2015	B2
9098528	Wang	Aug 2015	B2
9104477	Kodialam et al.	Aug 2015	B2
9110732	Forschmiedt et al.	Aug 2015	B1
9110770	Raju et al.	Aug 2015	B1
9111037	Nalis et al.	Aug 2015	B1
9112813	Jackson	Aug 2015	B2
9116733	Banga et al.	Aug 2015	B2
9141410	Leafe et al.	Sep 2015	B2
9146764	Wagner	Sep 2015	B1
9152406	De et al.	Oct 2015	B2
9164754	Pohlack	Oct 2015	B1
9183019	Kruglick	Nov 2015	B2
9195520	Turk	Nov 2015	B2
9208007	Harper et al.	Dec 2015	B2
9218190	Anand et al.	Dec 2015	B2
9223561	Orveillon et al.	Dec 2015	B2
9223966	Satish et al.	Dec 2015	B1
9250893	Blahaerath et al.	Feb 2016	B2
9268586	Voccio et al.	Feb 2016	B2
9298633	Zhao et al.	Mar 2016	B1
9317689	Aissi	Apr 2016	B2
9323556	Wagner	Apr 2016	B2
9361145	Wilson et al.	Jun 2016	B1
9405582	Fuller et al.	Aug 2016	B2
9411645	Duan et al.	Aug 2016	B1
9413626	Reque et al.	Aug 2016	B2
9417918	Chin et al.	Aug 2016	B2
9430290	Gupta et al.	Aug 2016	B1
9436555	Dornemann et al.	Sep 2016	B2
9461996	Hayton et al.	Oct 2016	B2
9471775	Wagner et al.	Oct 2016	B1
9471776	Gu et al.	Oct 2016	B2
9483335	Wagner et al.	Nov 2016	B1
9489227	Oh et al.	Nov 2016	B2
9497136	Ramarao et al.	Nov 2016	B1
9501345	Lietz et al.	Nov 2016	B1
9514037	Dow et al.	Dec 2016	B1
9537788	Reque et al.	Jan 2017	B2
9563613	Dinkel et al.	Feb 2017	B1
9575798	Terayama et al.	Feb 2017	B2
9588790	Wagner et al.	Mar 2017	B1
9594590	Hsu	Mar 2017	B2
9596350	Dymshyts et al.	Mar 2017	B1
9600312	Wagner et al.	Mar 2017	B2
9613127	Rus et al.	Apr 2017	B1
9626204	Banga et al.	Apr 2017	B1
9628332	Bruno, Jr. et al.	Apr 2017	B2
9635132	Lin et al.	Apr 2017	B1
9652306	Wagner et al.	May 2017	B1
9652617	Evans et al.	May 2017	B1
9654508	Barton et al.	May 2017	B2
9661011	Van Horenbeeck et al.	May 2017	B1
9678773	Wagner et al.	Jun 2017	B1
9678778	Youseff	Jun 2017	B1
9703681	Taylor et al.	Jul 2017	B2
9715402	Wagner et al.	Jul 2017	B2
9720661	Gschwind et al.	Aug 2017	B2
9720662	Gschwind et al.	Aug 2017	B2
9727725	Wagner et al.	Aug 2017	B2
9733967	Wagner et al.	Aug 2017	B2
9760387	Wagner et al.	Sep 2017	B2
9760443	Tarasuk-Levin et al.	Sep 2017	B2
9767271	Ghose	Sep 2017	B2
9785476	Wagner et al.	Oct 2017	B2
9787779	Frank et al.	Oct 2017	B2
9798831	Chattopadhyay et al.	Oct 2017	B2
9811363	Wagner	Nov 2017	B1
9811434	Wagner	Nov 2017	B1
9817695	Clark	Nov 2017	B2
9830175	Wagner	Nov 2017	B1
9830193	Wagner et al.	Nov 2017	B1
9830449	Wagner	Nov 2017	B1
9864636	Patel et al.	Jan 2018	B1
9898393	Moorthi et al.	Feb 2018	B2
9910713	Wisniewski et al.	Mar 2018	B2
9921864	Singaravelu et al.	Mar 2018	B2
9928108	Wagner et al.	Mar 2018	B1
9929916	Subramanian et al.	Mar 2018	B1
9930103	Thompson	Mar 2018	B2
9930133	Susarla et al.	Mar 2018	B2
9952896	Wagner et al.	Apr 2018	B2
9977691	Marriner et al.	May 2018	B2
9979817	Huang et al.	May 2018	B2
9983982	Kumar et al.	May 2018	B1
10002026	Wagner	Jun 2018	B1
10013267	Wagner et al.	Jul 2018	B1
10042660	Wagner et al.	Aug 2018	B2
10048974	Wagner et al.	Aug 2018	B1
10049302	Liu	Aug 2018	B1
10061613	Brooker et al.	Aug 2018	B1
10067801	Wagner	Sep 2018	B1
10102040	Marriner et al.	Oct 2018	B2
10108443	Wagner et al.	Oct 2018	B2
10139876	Lu et al.	Nov 2018	B2
10140137	Wagner	Nov 2018	B2
10146635	Chai et al.	Dec 2018	B1
10162655	Tuch et al.	Dec 2018	B2
10162672	Wagner et al.	Dec 2018	B2
10162688	Wagner	Dec 2018	B2
10191861	Steinberg	Jan 2019	B1
10193839	Tandon et al.	Jan 2019	B2
10198298	Bishop et al.	Feb 2019	B2
10203990	Wagner et al.	Feb 2019	B2
10248467	Wisniewski et al.	Apr 2019	B2
10255090	Tuch et al.	Apr 2019	B2
10277708	Wagner et al.	Apr 2019	B2
10282229	Wagner et al.	May 2019	B2
10282250	Banerjee	May 2019	B1
10303492	Wagner et al.	May 2019	B1
10331462	Varda et al.	Jun 2019	B1
10346625	Anderson et al.	Jul 2019	B2
10353678	Wagner	Jul 2019	B1
10353746	Reque et al.	Jul 2019	B2
10360025	Foskett et al.	Jul 2019	B2
10360067	Wagner	Jul 2019	B1
10365985	Wagner	Jul 2019	B2
10387177	Wagner et al.	Aug 2019	B2
10402231	Marriner et al.	Sep 2019	B2
10423158	Hadlich	Sep 2019	B1
10437629	Wagner et al.	Oct 2019	B2
10445140	Sagar et al.	Oct 2019	B1
10459822	Gondi	Oct 2019	B1
10503626	Idicula et al.	Dec 2019	B2
10528390	Brooker et al.	Jan 2020	B2
10531226	Wang et al.	Jan 2020	B1
10552193	Wagner et al.	Feb 2020	B2
10552442	Lusk et al.	Feb 2020	B1
10564946	Wagner et al.	Feb 2020	B1
10572375	Wagner	Feb 2020	B1
10592269	Wagner et al.	Mar 2020	B2
10608973	Kuo et al.	Mar 2020	B2
10615984	Wang	Apr 2020	B1
10623476	Thompson	Apr 2020	B2
10637817	Kuo et al.	Apr 2020	B2
10649749	Brooker et al.	May 2020	B1
10649792	Kulchytskyy et al.	May 2020	B1
10650156	Anderson et al.	May 2020	B2
10686605	Chhabra et al.	Jun 2020	B2
10691498	Wagner	Jun 2020	B2
10713080	Brooker et al.	Jul 2020	B1
10719367	Kim et al.	Jul 2020	B1
10725752	Wagner et al.	Jul 2020	B1
10725826	Sagar et al.	Jul 2020	B1
10733085	Wagner	Aug 2020	B1
10754701	Wagner	Aug 2020	B1
10776091	Wagner et al.	Sep 2020	B1
10776171	Wagner et al.	Sep 2020	B2
10817331	Mullen et al.	Oct 2020	B2
10824484	Wagner et al.	Nov 2020	B2
10831898	Wagner	Nov 2020	B1
10846117	Steinberg	Nov 2020	B1
10853112	Wagner et al.	Dec 2020	B2
10853115	Mullen et al.	Dec 2020	B2
10884722	Brooker et al.	Jan 2021	B2
10884787	Wagner et al.	Jan 2021	B1
10884802	Wagner et al.	Jan 2021	B2
10884812	Brooker et al.	Jan 2021	B2
10891145	Wagner et al.	Jan 2021	B2
10915371	Wagner et al.	Feb 2021	B2
10942795	Yanacek et al.	Mar 2021	B1
10949237	Piwonka et al.	Mar 2021	B2
10956185	Wagner	Mar 2021	B2
11010188	Brooker et al.	May 2021	B1
11016815	Wisniewski et al.	May 2021	B2
11099870	Brooker et al.	Aug 2021	B1
11099917	Hussels et al.	Aug 2021	B2
11115404	Siefker et al.	Sep 2021	B2
11119809	Brooker et al.	Sep 2021	B1
11119813	Kasaragod	Sep 2021	B1
11119826	Yanacek et al.	Sep 2021	B2
11126469	Reque et al.	Sep 2021	B2
11132213	Wagner	Sep 2021	B1
11146569	Brooker et al.	Oct 2021	B1
11159528	Siefker et al.	Oct 2021	B2
11188391	Sule	Nov 2021	B1
11190609	Siefker et al.	Nov 2021	B2
11243953	Wagner et al.	Feb 2022	B2
20010044817	Asano et al.	Nov 2001	A1
20020120685	Srivastava et al.	Aug 2002	A1
20020172273	Baker et al.	Nov 2002	A1
20030071842	King et al.	Apr 2003	A1
20030084434	Ren	May 2003	A1
20030149801	Kushnirskiy	Aug 2003	A1
20030191795	Bernardin et al.	Oct 2003	A1
20030208569	O'Brien et al.	Nov 2003	A1
20030229794	James, II et al.	Dec 2003	A1
20040003087	Chambliss et al.	Jan 2004	A1
20040019886	Berent et al.	Jan 2004	A1
20040044721	Song et al.	Mar 2004	A1
20040049768	Matsuyama et al.	Mar 2004	A1
20040098154	McCarthy	May 2004	A1
20040158551	Santosuosso	Aug 2004	A1
20040205493	Simpson et al.	Oct 2004	A1
20040249947	Novaes et al.	Dec 2004	A1
20040268358	Darling et al.	Dec 2004	A1
20050027611	Wharton	Feb 2005	A1
20050044301	Vasilevsky et al.	Feb 2005	A1
20050120160	Plouffe et al.	Jun 2005	A1
20050132167	Longobardi	Jun 2005	A1
20050132368	Sexton et al.	Jun 2005	A1
20050149535	Frey et al.	Jul 2005	A1
20050193113	Kokusho et al.	Sep 2005	A1
20050193283	Reinhardt et al.	Sep 2005	A1
20050237948	Wan et al.	Oct 2005	A1
20050257051	Richard	Nov 2005	A1
20050262183	Colrain et al.	Nov 2005	A1
20050262512	Schmidt et al.	Nov 2005	A1
20060010440	Anderson et al.	Jan 2006	A1
20060015740	Kramer	Jan 2006	A1
20060036941	Neil	Feb 2006	A1
20060080678	Bailey et al.	Apr 2006	A1
20060123066	Jacobs et al.	Jun 2006	A1
20060129684	Datta	Jun 2006	A1
20060155800	Matsumoto	Jul 2006	A1
20060168174	Gebhart et al.	Jul 2006	A1
20060184669	Vaidyanathan et al.	Aug 2006	A1
20060200668	Hybre et al.	Sep 2006	A1
20060212332	Jackson	Sep 2006	A1
20060218601	Michel	Sep 2006	A1
20060242647	Kimbrel et al.	Oct 2006	A1
20060242709	Seinfeld et al.	Oct 2006	A1
20060248195	Toumura et al.	Nov 2006	A1
20060259763	Cooperstein et al.	Nov 2006	A1
20060288120	Hoshino et al.	Dec 2006	A1
20070033085	Johnson	Feb 2007	A1
20070050779	Hayashi	Mar 2007	A1
20070094396	Takano et al.	Apr 2007	A1
20070101325	Bystricky et al.	May 2007	A1
20070112864	Ben-Natan	May 2007	A1
20070130341	Ma	Jun 2007	A1
20070174419	O'Connell et al.	Jul 2007	A1
20070180449	Croft et al.	Aug 2007	A1
20070180450	Croft et al.	Aug 2007	A1
20070180493	Croft et al.	Aug 2007	A1
20070186212	Mazzaferri et al.	Aug 2007	A1
20070192082	Gaos et al.	Aug 2007	A1
20070192329	Croft et al.	Aug 2007	A1
20070198656	Mazzaferri et al.	Aug 2007	A1
20070199000	Shekhel et al.	Aug 2007	A1
20070220009	Morris et al.	Sep 2007	A1
20070226700	Gal et al.	Sep 2007	A1
20070240160	Paterson-Jones	Oct 2007	A1
20070255604	Seelig	Nov 2007	A1
20080028409	Cherkasova et al.	Jan 2008	A1
20080052401	Bugenhagen et al.	Feb 2008	A1
20080052725	Stoodley et al.	Feb 2008	A1
20080082977	Araujo et al.	Apr 2008	A1
20080104247	Venkatakrishnan et al.	May 2008	A1
20080104608	Hyser et al.	May 2008	A1
20080115143	Shimizu et al.	May 2008	A1
20080126110	Haeberle et al.	May 2008	A1
20080126486	Heist	May 2008	A1
20080127125	Anckaert et al.	May 2008	A1
20080147893	Marripudi et al.	Jun 2008	A1
20080189468	Schmidt et al.	Aug 2008	A1
20080195369	Duyanovich et al.	Aug 2008	A1
20080201568	Quinn et al.	Aug 2008	A1
20080201711	Amir Husain	Aug 2008	A1
20080209423	Hirai	Aug 2008	A1
20080244547	Wintergerst et al.	Oct 2008	A1
20080288940	Adams et al.	Nov 2008	A1
20080307098	Kelly	Dec 2008	A1
20090006897	Sarsfield	Jan 2009	A1
20090013153	Hilton	Jan 2009	A1
20090025009	Brunswig et al.	Jan 2009	A1
20090034537	Colrain et al.	Feb 2009	A1
20090055810	Kondur	Feb 2009	A1
20090055829	Gibson	Feb 2009	A1
20090070355	Cadarette et al.	Mar 2009	A1
20090077569	Appleton et al.	Mar 2009	A1
20090125902	Ghosh et al.	May 2009	A1
20090158275	Wang et al.	Jun 2009	A1
20090158407	Nicodemus et al.	Jun 2009	A1
20090177860	Zhu et al.	Jul 2009	A1
20090183162	Kindel et al.	Jul 2009	A1
20090193410	Arthursson et al.	Jul 2009	A1
20090198769	Keller et al.	Aug 2009	A1
20090204960	Ben-yehuda et al.	Aug 2009	A1
20090204964	Foley et al.	Aug 2009	A1
20090222922	Sidiroglou et al.	Sep 2009	A1
20090271472	Scheifler et al.	Oct 2009	A1
20090288084	Astete et al.	Nov 2009	A1
20090300151	Friedman et al.	Dec 2009	A1
20090300599	Piotrowski	Dec 2009	A1
20090307430	Bruening et al.	Dec 2009	A1
20100023940	Iwamatsu et al.	Jan 2010	A1
20100031274	Sim-Tang	Feb 2010	A1
20100031325	Maigne et al.	Feb 2010	A1
20100036925	Haffner	Feb 2010	A1
20100037031	DeSantis et al.	Feb 2010	A1
20100058342	Machida	Mar 2010	A1
20100058351	Yahagi	Mar 2010	A1
20100064299	Kacin et al.	Mar 2010	A1
20100070678	Zhang et al.	Mar 2010	A1
20100070725	Prahlad et al.	Mar 2010	A1
20100083048	Calinoiu et al.	Apr 2010	A1
20100083248	Wood et al.	Apr 2010	A1
20100094816	Groves, Jr. et al.	Apr 2010	A1
20100106926	Kandasamy et al.	Apr 2010	A1
20100114825	Siddegowda	May 2010	A1
20100115098	De Baer et al.	May 2010	A1
20100122343	Ghosh	May 2010	A1
20100131936	Cheriton	May 2010	A1
20100131959	Spiers et al.	May 2010	A1
20100186011	Magenheimer	Jul 2010	A1
20100198972	Umbehocker	Aug 2010	A1
20100199285	Medovich	Aug 2010	A1
20100257116	Mehta et al.	Oct 2010	A1
20100257269	Clark	Oct 2010	A1
20100269109	Cartales	Oct 2010	A1
20100299541	Ishikawa et al.	Nov 2010	A1
20100312871	Desantis et al.	Dec 2010	A1
20100325727	Neystadt et al.	Dec 2010	A1
20100329149	Singh et al.	Dec 2010	A1
20100329643	Kuang	Dec 2010	A1
20110004687	Takemura	Jan 2011	A1
20110010690	Howard et al.	Jan 2011	A1
20110010722	Matsuyama	Jan 2011	A1
20110023026	Oza	Jan 2011	A1
20110029970	Arasaratnam	Feb 2011	A1
20110029984	Norman et al.	Feb 2011	A1
20110040812	Phillips	Feb 2011	A1
20110055378	Ferris et al.	Mar 2011	A1
20110055396	DeHaan	Mar 2011	A1
20110055683	Jiang	Mar 2011	A1
20110078679	Bozek et al.	Mar 2011	A1
20110099204	Thaler	Apr 2011	A1
20110099551	Fahrig et al.	Apr 2011	A1
20110131572	Elyashev et al.	Jun 2011	A1
20110134761	Smith	Jun 2011	A1
20110141124	Halls et al.	Jun 2011	A1
20110153541	Koch et al.	Jun 2011	A1
20110153727	Li	Jun 2011	A1
20110153838	Belkine et al.	Jun 2011	A1
20110154353	Theroux et al.	Jun 2011	A1
20110173637	Brandwine et al.	Jul 2011	A1
20110179162	Mayo et al.	Jul 2011	A1
20110184993	Chawla et al.	Jul 2011	A1
20110225277	Freimuth et al.	Sep 2011	A1
20110231680	Padmanabhan et al.	Sep 2011	A1
20110247005	Benedetti et al.	Oct 2011	A1
20110258603	Wisnovsky et al.	Oct 2011	A1
20110265067	Schulte et al.	Oct 2011	A1
20110265069	Fee et al.	Oct 2011	A1
20110265164	Lucovsky	Oct 2011	A1
20110271276	Ashok et al.	Nov 2011	A1
20110276945	Chasman et al.	Nov 2011	A1
20110276963	Wu et al.	Nov 2011	A1
20110296412	Banga et al.	Dec 2011	A1
20110314465	Smith et al.	Dec 2011	A1
20110321033	Kelkar et al.	Dec 2011	A1
20110321051	Rastogi	Dec 2011	A1
20120011496	Shimamura	Jan 2012	A1
20120011511	Horvitz et al.	Jan 2012	A1
20120016721	Weinman	Jan 2012	A1
20120041970	Ghosh et al.	Feb 2012	A1
20120054744	Singh et al.	Mar 2012	A1
20120060207	Mardikar et al.	Mar 2012	A1
20120072762	Atchison et al.	Mar 2012	A1
20120072914	Ota	Mar 2012	A1
20120072920	Kawamura	Mar 2012	A1
20120079004	Herman	Mar 2012	A1
20120096271	Ramarathinam et al.	Apr 2012	A1
20120096468	Chakravorty et al.	Apr 2012	A1
20120101952	Raleigh	Apr 2012	A1
20120102307	Wong	Apr 2012	A1
20120102333	Wong	Apr 2012	A1
20120102481	Mani et al.	Apr 2012	A1
20120102493	Allen et al.	Apr 2012	A1
20120110155	Adlung et al.	May 2012	A1
20120110164	Frey et al.	May 2012	A1
20120110570	Jacobson et al.	May 2012	A1
20120110588	Bieswanger et al.	May 2012	A1
20120131379	Tameshige et al.	May 2012	A1
20120144290	Goldman et al.	Jun 2012	A1
20120166624	Suit et al.	Jun 2012	A1
20120173709	Li et al.	Jul 2012	A1
20120192184	Burckart et al.	Jul 2012	A1
20120197795	Campbell et al.	Aug 2012	A1
20120197958	Nightingale et al.	Aug 2012	A1
20120198442	Kashyap et al.	Aug 2012	A1
20120198514	McCune et al.	Aug 2012	A1
20120204164	Castanos et al.	Aug 2012	A1
20120209947	Glaser et al.	Aug 2012	A1
20120222038	Katragadda et al.	Aug 2012	A1
20120233464	Miller et al.	Sep 2012	A1
20120254193	Chattopadhyay et al.	Oct 2012	A1
20120324236	Srivastava et al.	Dec 2012	A1
20120331113	Jain et al.	Dec 2012	A1
20130014101	Ballani et al.	Jan 2013	A1
20130042234	DeLuca et al.	Feb 2013	A1
20130054804	Jana et al.	Feb 2013	A1
20130054927	Raj et al.	Feb 2013	A1
20130055262	Lubsey et al.	Feb 2013	A1
20130061208	Tsao et al.	Mar 2013	A1
20130061212	Krause et al.	Mar 2013	A1
20130061220	Gnanasambandam et al.	Mar 2013	A1
20130067484	Sonoda et al.	Mar 2013	A1
20130067494	Srour et al.	Mar 2013	A1
20130080641	Lui et al.	Mar 2013	A1
20130091387	Bohnet et al.	Apr 2013	A1
20130097601	Podvratnik et al.	Apr 2013	A1
20130111032	Alapati et al.	May 2013	A1
20130111469	B et al.	May 2013	A1
20130124807	Nielsen et al.	May 2013	A1
20130132942	Wang	May 2013	A1
20130132953	Chuang et al.	May 2013	A1
20130139152	Chang et al.	May 2013	A1
20130139166	Zhang et al.	May 2013	A1
20130145354	Bruening et al.	Jun 2013	A1
20130151587	Takeshima et al.	Jun 2013	A1
20130151648	Luna	Jun 2013	A1
20130151684	Forsman et al.	Jun 2013	A1
20130152047	Moorthi et al.	Jun 2013	A1
20130167147	Corrie et al.	Jun 2013	A1
20130179574	Calder et al.	Jul 2013	A1
20130179881	Calder et al.	Jul 2013	A1
20130179894	Calder et al.	Jul 2013	A1
20130179895	Calder et al.	Jul 2013	A1
20130185719	Kar et al.	Jul 2013	A1
20130185729	Vasic et al.	Jul 2013	A1
20130191924	Tedesco	Jul 2013	A1
20130198319	Shen et al.	Aug 2013	A1
20130198743	Kruglick	Aug 2013	A1
20130198748	Sharp et al.	Aug 2013	A1
20130198763	Kunze et al.	Aug 2013	A1
20130205092	Roy et al.	Aug 2013	A1
20130219390	Lee et al.	Aug 2013	A1
20130227097	Yasuda et al.	Aug 2013	A1
20130227534	Ike et al.	Aug 2013	A1
20130227563	McGrath	Aug 2013	A1
20130227641	White et al.	Aug 2013	A1
20130227710	Barak et al.	Aug 2013	A1
20130232190	Miller et al.	Sep 2013	A1
20130232480	Winterfeldt et al.	Sep 2013	A1
20130239125	Iorio	Sep 2013	A1
20130246944	Pandiyan et al.	Sep 2013	A1
20130262556	Xu et al.	Oct 2013	A1
20130263117	Konik et al.	Oct 2013	A1
20130274006	Hudlow et al.	Oct 2013	A1
20130275376	Hudlow et al.	Oct 2013	A1
20130275958	Ivanov et al.	Oct 2013	A1
20130275969	Dimitrov	Oct 2013	A1
20130275975	Masuda et al.	Oct 2013	A1
20130283141	Stevenson et al.	Oct 2013	A1
20130283176	Hoole et al.	Oct 2013	A1
20130290538	Gmach et al.	Oct 2013	A1
20130291087	Kailash et al.	Oct 2013	A1
20130297964	Hegdal et al.	Nov 2013	A1
20130298183	McGrath et al.	Nov 2013	A1
20130311650	Brandwine et al.	Nov 2013	A1
20130326506	McGrath et al.	Dec 2013	A1
20130326507	McGrath et al.	Dec 2013	A1
20130339950	Ramarathinam et al.	Dec 2013	A1
20130346470	Obstfeld et al.	Dec 2013	A1
20130346946	Pinnix	Dec 2013	A1
20130346952	Huang et al.	Dec 2013	A1
20130346964	Nobuoka et al.	Dec 2013	A1
20130346987	Raney et al.	Dec 2013	A1
20130346994	Chen et al.	Dec 2013	A1
20130347095	Barjatiya et al.	Dec 2013	A1
20140007097	Chin et al.	Jan 2014	A1
20140019523	Heymann et al.	Jan 2014	A1
20140019735	Menon et al.	Jan 2014	A1
20140019965	Neuse et al.	Jan 2014	A1
20140019966	Neuse et al.	Jan 2014	A1
20140040343	Nickolov et al.	Feb 2014	A1
20140040857	Trinchini et al.	Feb 2014	A1
20140040880	Brownlow et al.	Feb 2014	A1
20140047437	Wu et al.	Feb 2014	A1
20140058871	Marr et al.	Feb 2014	A1
20140059209	Alnoor	Feb 2014	A1
20140059226	Messerli et al.	Feb 2014	A1
20140059552	Cunningham et al.	Feb 2014	A1
20140068568	Wisnovsky	Mar 2014	A1
20140068608	Kulkarni	Mar 2014	A1
20140068611	McGrath et al.	Mar 2014	A1
20140073300	Leeder et al.	Mar 2014	A1
20140081984	Sitsky et al.	Mar 2014	A1
20140082165	Marr et al.	Mar 2014	A1
20140082201	Shankari et al.	Mar 2014	A1
20140101643	Inoue	Apr 2014	A1
20140101649	Kamble et al.	Apr 2014	A1
20140108722	Lipchuk et al.	Apr 2014	A1
20140109087	Jujare et al.	Apr 2014	A1
20140109088	Dournov et al.	Apr 2014	A1
20140129667	Ozawa	May 2014	A1
20140130040	Lemanski	May 2014	A1
20140137110	Engle et al.	May 2014	A1
20140173614	Konik et al.	Jun 2014	A1
20140173616	Bird et al.	Jun 2014	A1
20140180862	Certain et al.	Jun 2014	A1
20140189677	Curzi et al.	Jul 2014	A1
20140189704	Narvaez et al.	Jul 2014	A1
20140201735	Kannan et al.	Jul 2014	A1
20140207912	Thibeault	Jul 2014	A1
20140214752	Rash et al.	Jul 2014	A1
20140215073	Dow et al.	Jul 2014	A1
20140229221	Shih et al.	Aug 2014	A1
20140229942	Wiseman et al.	Aug 2014	A1
20140245297	Hackett	Aug 2014	A1
20140279581	Devereaux	Sep 2014	A1
20140280325	Krishnamurthy et al.	Sep 2014	A1
20140282418	Wood et al.	Sep 2014	A1
20140282559	Verduzco et al.	Sep 2014	A1
20140282615	Cavage et al.	Sep 2014	A1
20140282629	Gupta et al.	Sep 2014	A1
20140283045	Brandwine et al.	Sep 2014	A1
20140289286	Gusak	Sep 2014	A1
20140298295	Overbeck	Oct 2014	A1
20140304246	Helmich et al.	Oct 2014	A1
20140304698	Chigurapati et al.	Oct 2014	A1
20140304815	Maeda	Oct 2014	A1
20140317617	O'Donnell	Oct 2014	A1
20140337953	Banatwala et al.	Nov 2014	A1
20140344457	Bruno, Jr. et al.	Nov 2014	A1
20140344736	Ryman et al.	Nov 2014	A1
20140359093	Raju et al.	Dec 2014	A1
20140365781	Dmitrienko et al.	Dec 2014	A1
20140372489	Jaiswal et al.	Dec 2014	A1
20140372533	Fu et al.	Dec 2014	A1
20140380085	Rash et al.	Dec 2014	A1
20150033241	Jackson et al.	Jan 2015	A1
20150039891	Ignatchenko et al.	Feb 2015	A1
20150040229	Chan et al.	Feb 2015	A1
20150046926	Kenchammana-Hosekote et al.	Feb 2015	A1
20150046971	Huh et al.	Feb 2015	A1
20150052258	Johnson et al.	Feb 2015	A1
20150058914	Yadav	Feb 2015	A1
20150067019	Balko	Mar 2015	A1
20150067830	Johansson et al.	Mar 2015	A1
20150074659	Madsen et al.	Mar 2015	A1
20150074661	Kothari et al.	Mar 2015	A1
20150074662	Saladi et al.	Mar 2015	A1
20150081885	Thomas et al.	Mar 2015	A1
20150095822	Feis et al.	Apr 2015	A1
20150106805	Melander et al.	Apr 2015	A1
20150120928	Gummaraju et al.	Apr 2015	A1
20150121391	Wang	Apr 2015	A1
20150134626	Theimer et al.	May 2015	A1
20150135287	Medeiros et al.	May 2015	A1
20150142747	Zou	May 2015	A1
20150142952	Bragstad et al.	May 2015	A1
20150143374	Banga et al.	May 2015	A1
20150143381	Chin et al.	May 2015	A1
20150146716	Olivier et al.	May 2015	A1
20150154046	Farkas et al.	Jun 2015	A1
20150161384	Gu et al.	Jun 2015	A1
20150163231	Sobko et al.	Jun 2015	A1
20150178019	Hegdal et al.	Jun 2015	A1
20150178110	Li et al.	Jun 2015	A1
20150186129	Apte et al.	Jul 2015	A1
20150188775	Van Der Walt et al.	Jul 2015	A1
20150199218	Wilson et al.	Jul 2015	A1
20150205596	Hiltegen et al.	Jul 2015	A1
20150227598	Hahn et al.	Aug 2015	A1
20150229645	Keith et al.	Aug 2015	A1
20150235144	Gusev et al.	Aug 2015	A1
20150242225	Muller et al.	Aug 2015	A1
20150254248	Burns et al.	Sep 2015	A1
20150256621	Noda et al.	Sep 2015	A1
20150261578	Greden et al.	Sep 2015	A1
20150264014	Budhani et al.	Sep 2015	A1
20150269494	Kardes et al.	Sep 2015	A1
20150271280	Zhang et al.	Sep 2015	A1
20150289220	Kim et al.	Oct 2015	A1
20150309923	Iwata et al.	Oct 2015	A1
20150319160	Ferguson et al.	Nov 2015	A1
20150324174	Bromley et al.	Nov 2015	A1
20150324182	Barros et al.	Nov 2015	A1
20150324229	Valine	Nov 2015	A1
20150332048	Mooring et al.	Nov 2015	A1
20150332195	Jue	Nov 2015	A1
20150334173	Coulmeau et al.	Nov 2015	A1
20150350701	Lemus et al.	Dec 2015	A1
20150356294	Tan et al.	Dec 2015	A1
20150363181	Alberti et al.	Dec 2015	A1
20150363304	Nagamalla et al.	Dec 2015	A1
20150370560	Tan et al.	Dec 2015	A1
20150370591	Tuch et al.	Dec 2015	A1
20150370592	Tuch et al.	Dec 2015	A1
20150371244	Neuse et al.	Dec 2015	A1
20150378762	Saladi et al.	Dec 2015	A1
20150378764	Sivasubramanian et al.	Dec 2015	A1
20150378765	Singh et al.	Dec 2015	A1
20150379167	Griffith et al.	Dec 2015	A1
20160011901	Hurwitz et al.	Jan 2016	A1
20160012099	Tuatini et al.	Jan 2016	A1
20160019081	Chandrasekaran et al.	Jan 2016	A1
20160019082	Chandrasekaran et al.	Jan 2016	A1
20160019536	Ortiz et al.	Jan 2016	A1
20160021112	Katieb	Jan 2016	A1
20160026486	Abdallah	Jan 2016	A1
20160048606	Rubinstein et al.	Feb 2016	A1
20160070714	D'Sa et al.	Mar 2016	A1
20160072727	Leafe et al.	Mar 2016	A1
20160077901	Roth et al.	Mar 2016	A1
20160092320	Baca	Mar 2016	A1
20160092493	Ko et al.	Mar 2016	A1
20160098285	Davis et al.	Apr 2016	A1
20160100036	Lo et al.	Apr 2016	A1
20160103739	Huang et al.	Apr 2016	A1
20160110188	Verde et al.	Apr 2016	A1
20160117163	Fukui et al.	Apr 2016	A1
20160117254	Susarla et al.	Apr 2016	A1
20160119289	Jain et al.	Apr 2016	A1
20160124665	Jain et al.	May 2016	A1
20160124978	Nithrakashyap et al.	May 2016	A1
20160140180	Park et al.	May 2016	A1
20160150053	Janczuk et al.	May 2016	A1
20160162478	Blassin	Jun 2016	A1
20160188367	Zeng	Jun 2016	A1
20160191420	Nagarajan et al.	Jun 2016	A1
20160203219	Hoch et al.	Jul 2016	A1
20160212007	Alatorre et al.	Jul 2016	A1
20160226955	Moorthi et al.	Aug 2016	A1
20160282930	Ramachandran et al.	Sep 2016	A1
20160285906	Fine et al.	Sep 2016	A1
20160292016	Bussard et al.	Oct 2016	A1
20160294614	Searle et al.	Oct 2016	A1
20160306613	Busi et al.	Oct 2016	A1
20160315910	Kaufman	Oct 2016	A1
20160350099	Suparna et al.	Dec 2016	A1
20160357536	Firlik et al.	Dec 2016	A1
20160364265	Cao et al.	Dec 2016	A1
20160364316	Bhat et al.	Dec 2016	A1
20160371127	Antony et al.	Dec 2016	A1
20160371156	Merriman	Dec 2016	A1
20160378449	Khazanchi et al.	Dec 2016	A1
20160378547	Brouwer et al.	Dec 2016	A1
20160378554	Gummaraju et al.	Dec 2016	A1
20170004169	Merrill et al.	Jan 2017	A1
20170041144	Krapf et al.	Feb 2017	A1
20170041309	Ekambaram et al.	Feb 2017	A1
20170060615	Thakkar et al.	Mar 2017	A1
20170060621	Whipple et al.	Mar 2017	A1
20170068574	Cherkasova et al.	Mar 2017	A1
20170075749	Ambichl et al.	Mar 2017	A1
20170083381	Cong et al.	Mar 2017	A1
20170085447	Chen et al.	Mar 2017	A1
20170085502	Biruduraju	Mar 2017	A1
20170085591	Ganda et al.	Mar 2017	A1
20170093684	Jayaraman et al.	Mar 2017	A1
20170093920	Ducatel et al.	Mar 2017	A1
20170134519	Chen et al.	May 2017	A1
20170147656	Choudhary et al.	May 2017	A1
20170149740	Mansour et al.	May 2017	A1
20170161059	Wood et al.	Jun 2017	A1
20170177854	Gligor et al.	Jun 2017	A1
20170188213	Nirantar et al.	Jun 2017	A1
20170221000	Anand	Aug 2017	A1
20170230262	Sreeramoju et al.	Aug 2017	A1
20170230499	Mumick et al.	Aug 2017	A1
20170249130	Smiljamic et al.	Aug 2017	A1
20170264681	Apte et al.	Sep 2017	A1
20170272462	Kraemer et al.	Sep 2017	A1
20170286187	Chen et al.	Oct 2017	A1
20170308520	Beahan, Jr. et al.	Oct 2017	A1
20170315163	Wang et al.	Nov 2017	A1
20170329578	Iscen	Nov 2017	A1
20170346808	Anzai et al.	Nov 2017	A1
20170353851	Gonzalez et al.	Dec 2017	A1
20170364345	Fontoura et al.	Dec 2017	A1
20170371720	Basu et al.	Dec 2017	A1
20170372142	Bilobrov	Dec 2017	A1
20180004555	Ramanathan et al.	Jan 2018	A1
20180004556	Marriner et al.	Jan 2018	A1
20180004575	Marriner et al.	Jan 2018	A1
20180046453	Nair et al.	Feb 2018	A1
20180046482	Karve et al.	Feb 2018	A1
20180060132	Maru et al.	Mar 2018	A1
20180060221	Yim et al.	Mar 2018	A1
20180060318	Yang et al.	Mar 2018	A1
20180067841	Mahimkar	Mar 2018	A1
20180067873	Pikhur et al.	Mar 2018	A1
20180069702	Ayyadevara et al.	Mar 2018	A1
20180081717	Li	Mar 2018	A1
20180089232	Spektor et al.	Mar 2018	A1
20180095738	Dürkop et al.	Apr 2018	A1
20180121665	Anderson et al.	May 2018	A1
20180129684	Wilson et al.	May 2018	A1
20180150339	Pan et al.	May 2018	A1
20180152401	Tandon et al.	May 2018	A1
20180152405	Kuo et al.	May 2018	A1
20180152406	Kuo et al.	May 2018	A1
20180192101	Bilobrov	Jul 2018	A1
20180225096	Mishra et al.	Aug 2018	A1
20180239636	Arora et al.	Aug 2018	A1
20180253333	Gupta	Sep 2018	A1
20180268130	Ghosh et al.	Sep 2018	A1
20180275987	Vandeputte	Sep 2018	A1
20180285101	Yahav et al.	Oct 2018	A1
20180300111	Bhat et al.	Oct 2018	A1
20180314845	Anderson et al.	Nov 2018	A1
20180316552	Subramani Nadar et al.	Nov 2018	A1
20180341504	Kissell	Nov 2018	A1
20180365422	Callaghan et al.	Dec 2018	A1
20180375781	Chen et al.	Dec 2018	A1
20190004866	Du et al.	Jan 2019	A1
20190028552	Johnson, II et al.	Jan 2019	A1
20190043231	Uzgin et al.	Feb 2019	A1
20190072529	Andrawes et al.	Mar 2019	A1
20190073430	Webster	Mar 2019	A1
20190079751	Foskett et al.	Mar 2019	A1
20190140831	De Lima Junior et al.	May 2019	A1
20190141015	Nellen	May 2019	A1
20190147085	Pal et al.	May 2019	A1
20190155629	Wagner et al.	May 2019	A1
20190171423	Mishra et al.	Jun 2019	A1
20190179678	Banerjee et al.	Jun 2019	A1
20190179725	Mital et al.	Jun 2019	A1
20190180036	Shukla	Jun 2019	A1
20190188288	Holm et al.	Jun 2019	A1
20190196795	Cavalier	Jun 2019	A1
20190196884	Wagner	Jun 2019	A1
20190235848	Swiecki et al.	Aug 2019	A1
20190238590	Talukdar et al.	Aug 2019	A1
20190250937	Thomas et al.	Aug 2019	A1
20190268152	Sandoval et al.	Aug 2019	A1
20190286475	Mani	Sep 2019	A1
20190286492	Gulsvig Wood et al.	Sep 2019	A1
20190303117	Kocberber et al.	Oct 2019	A1
20190311115	Lavi et al.	Oct 2019	A1
20190318312	Foskett et al.	Oct 2019	A1
20190361802	Li et al.	Nov 2019	A1
20190363885	Schiavoni et al.	Nov 2019	A1
20200007456	Greenstein et al.	Jan 2020	A1
20200026527	Xu et al.	Jan 2020	A1
20200028936	Gupta et al.	Jan 2020	A1
20200057680	Marriner et al.	Feb 2020	A1
20200065079	Kocberber et al.	Feb 2020	A1
20200073770	Mortimore, Jr. et al.	Mar 2020	A1
20200073987	Perumala et al.	Mar 2020	A1
20200081745	Cybulski et al.	Mar 2020	A1
20200110691	Bryant et al.	Apr 2020	A1
20200120120	Cybulski	Apr 2020	A1
20200136933	Raskar	Apr 2020	A1
20200153897	Mestery et al.	May 2020	A1
20200167208	Floes et al.	May 2020	A1
20200213151	Srivatsan et al.	Jul 2020	A1
20200327236	Pratt et al.	Oct 2020	A1
20200341799	Wagner et al.	Oct 2020	A1
20200366587	White et al.	Nov 2020	A1
20210081233	Mullen et al.	Mar 2021	A1
20210117534	Maximov et al.	Apr 2021	A1
20210232415	Wagner et al.	Jul 2021	A1
20210342125	Burnett	Nov 2021	A1
20210342785	Mann	Nov 2021	A1
20210389963	Wagner	Dec 2021	A1

Foreign Referenced Citations (66)

Number	Date	Country
2975522	Aug 2016	CA
1341238	Mar 2002	CN
101002170	Jul 2007	CN
101267334	Sep 2008	CN
101345757	Jan 2009	CN
101496005	Jul 2009	CN
101627388	Jan 2010	CN
101640700	Feb 2010	CN
102420846	Apr 2012	CN
103098027	May 2013	CN
103384237	Nov 2013	CN
103731427	Apr 2014	CN
104243479	Dec 2014	CN
105122243	Dec 2015	CN
112513813	Mar 2021	CN
2663052	Nov 2013	EP
3201762	Aug 2017	EP
3254434	Dec 2017	EP
3356938	Aug 2018	EP
3201768	Dec 2019	EP
3811209	Apr 2021	EP
3814895	May 2021	EP
3857375	Aug 2021	EP
2002-287974	Oct 2002	JP
2006-107599	Apr 2006	JP
2007-080161	Mar 2007	JP
2007-538323	Dec 2007	JP
2010-026562	Feb 2010	JP
2011-065243	Mar 2011	JP
2011-233146	Nov 2011	JP
2011-257847	Dec 2011	JP
2013-156996	Aug 2013	JP
2014-525624	Sep 2014	JP
2017-534107	Nov 2017	JP
2017-534967	Nov 2017	JP
2018-503896	Feb 2018	JP
2018-512087	May 2018	JP
2018-536213	Dec 2018	JP
10-357850	Oct 2002	KR
WO 2008114454	Sep 2008	WO
WO 2009137567	Nov 2009	WO
WO 2012039834	Mar 2012	WO
WO 2012050772	Apr 2012	WO
WO 2013106257	Jul 2013	WO
WO 2015078394	Jun 2015	WO
WO 2015108539	Jul 2015	WO
WO 2015149017	Oct 2015	WO
WO 2016053950	Apr 2016	WO
WO 2016053968	Apr 2016	WO
WO 2016053973	Apr 2016	WO
WO 2016090292	Jun 2016	WO
WO 2016126731	Aug 2016	WO
WO 2016164633	Oct 2016	WO
WO 2016164638	Oct 2016	WO
WO 2017059248	Apr 2017	WO
WO 2017112526	Jun 2017	WO
WO 2017172440	Oct 2017	WO
WO 2018005829	Jan 2018	WO
WO 2018098443	May 2018	WO
WO 2018098445	May 2018	WO
WO 2020005764	Jan 2020	WO
WO 2020006081	Jan 2020	WO
WO 2020069104	Apr 2020	WO
WO 2020123439	Jun 2020	WO
WO 2020264431	Dec 2020	WO
WO 2021108435	Jun 2021	WO

Non-Patent Literature Citations (134)

Entry
Anonymous: “Docker run reference”, Dec. 7, 2015, XP055350246, Retrieved from the Internet: URL:https://web.archive.org/web/20151207111702/https:/docs.docker.com/engine/reference/run/ [retrieved on Feb. 28, 2017].
Adapter Pattern, Wikipedia, https://en.wikipedia.org/w/index.php?title=Adapter_pattern&oldid=654971255, [retrieved May 26, 2016], 6 pages.
Amazon, “AWS Lambda: Developer Guide”, Jun. 26, 2016 Retrieved from the Internet, URL:http://docs.aws.amazon.com/lambda/latest/dg/lambda-dg.pdf, [retrieved on Aug. 30, 2017], 314 pages.
Amazon, “AWS Lambda: Developer Guide”, Apr. 30, 2016 Retrieved from the Internet, URL: https://web.archive.org/web/20160430050158/http://docs.aws.amazon.com:80/lambda/latest/dg/lambda-dg.pdf, 346 pages.
Amazon, “AWS Lambda: Developer Guide”, Retrieved from the Internet, 2019, URL : http://docs.aws.amazon.com/lambda/ latest/dg/lambda-dg.pdf, 521 pages.
Balazinska et al., Moirae: History-Enhanced Monitoring, Published: Jan. 2007, 12 pages.
Bebenita et al., “Trace-Based Compilation in Execution Environments without Interpreters,” ACM, Copyright 2010, 10 pages.
Ben-Yehuda et al., “Deconstructing Amazon EC2 Spot Instance Pricing”, ACM Transactions on Economics and Computation 1.3, Sep. 2013, 15 pages.
Bhadani et al., Performance evaluation of web servers using central load balancing policy over virtual machines on cloud, Jan. 2010, 4 pages.
Bryan Liston, “Ad Hoc Big Data Processing Made Simple with Serverless Map Reduce”, Nov. 4, 2016, Amazon Web Services <https :/laws. amazon .com/bl ogs/compute/ad-hoc-big-data-processi ng-made-si mple-with-serverless-mapred uce >.
CodeChef ADMIN discussion web page, retrieved from https://discuss.codechef.com/t/what-are-the-memory-limit-and-stack-size-on-codechef/14159, retrieved on Sep. 10, 2019.
CodeChef IDE web page, Code, Compile & Run, retrieved from https://www.codechef.com/ide, retrieved on Sep. 9, 2019.
Czajkowski, G., and L. Daynes, Multitasking Without Compromise: A Virtual Machine Evolution 47(4a):60-73, ACM Sigplan Notices—Supplemental Issue, Apr. 2012.
Das et al., Adaptive Stream Processing using Dynamic Batch Sizing, Nov. 2014, 13 pages.
Deis, Container, Jun. 2014, 1 page.
Dean et al, “MapReduce: Simplified Data Processing on Large Clusters”, ACM, 2008, pp. 107-113.
Dombrowski, M., et al., Dynamic Monitor Allocation in the Java Virtual Machine, JTRES '13, Oct. 9-11, 2013, pp. 30-37.
Dornemann et al., “On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud”, 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009, pp. 140-147.
Dynamic HTML, Wikipedia page from date Mar. 27, 2015, retrieved using the WayBackMachine, from https://web.archive.org/web/20150327215418/https://en.wikipedia.org/wiki/Dynamic_HTML, 2015, 6 pages.
Ekanayake et al, “Twister: A Runtime for Iterative MapReduce”, ACM, 2010, pp. 810-818.
Espadas, J., et al., A Tenant-Based Resource Allocation Model for Scaling Software-as-a-Service Applications Over Cloud Computing Infrastructures, Future Generation Computer Systems, vol. 29, pp. 273-286, 2013.
Fan et al., Online Optimization of VM Deployment in laaS Cloud, Dec. 17, 2012-Dec. 19, 2012, 6 pages.
Ha et al., A Concurrent Trace-based Just-In-Time Compiler for Single-threaded JavaScript, utexas.edu, Jun. 2009.
Hammoud et al, “Locality-Aware Reduce Task Scheduling for MapReduce”, IEEE, 2011, pp. 570-576.
Han et al., Lightweight Resource Scaling for Cloud Applications, May 13, 2012-May 16, 2012, 8 pages.
Hoffman, Auto scaling your website with Amazon Web Services (AWS)—Part 2, Cardinalpath, Sep. 2015, 15 pages.
http://discuss.codechef.com discussion web page from date Nov. 11, 2012, retrieved using the WayBackMachine, from https://web.archive.org/web/20121111040051/http://discuss.codechef.com/questions/2881 /why-are-simple-java-programs-using-up-so-much-space, 2012.
https://www.codechef.com code error help page from Jan. 2014, retrieved from https://www.codechef.com/JAN14/status/ERROR,val23, 2014.
http://www.codechef.com/ide web page from date Apr. 5, 2015, retrieved using the WayBackMachine, from https://web.archive.org/web/20150405045518/http://www.codechef.com/ide, 2015.
Huang, Zhe, Danny HK Tsang, and James She. “A virtual machine consolidation framework for mapreduce enabled computing clouds.” 2012 24th International Teletraffic Congress (ITC 24). IEEE, Sep. 4, 2012-Sep. 7, 2012.
Kamga et al., Extended scheduler for efficient frequency scaling in virtualized systems, Jul. 2012, 8 pages.
Kato, et al. “Web Service Conversion Architecture of the Web Application and Evaluation”; Research Report from Information Processing Society, Apr. 3, 2006 with Machine Translation.
Kazempour et al., AASH: an asymmetry-aware scheduler for hypervisors, Jul. 2010, 12 pages.
Kim et al., “MRBench: A Benchmark for Map-Reduce Framework”, IEEE, 2008, pp. 11-18.
Kraft et al., 10 performance prediction in consolidated virtualized environments, Mar. 2011, 12 pages.
Krsul et al., “VMPlants: Providing and Managing Virtual Machine Execution Environments for Grid Computing”, Supercomputing, 2004. Proceedings of the ACM/IEEESC 2004 Conference Pittsburgh, PA, XP010780332, Nov. 6-12, 2004, 12 pages.
Lagar-Cavilla et al., “SnowFlock: Virtual Machine Cloning as a First-Class Cloud Primitive”, ACM Transactions on Computer Systems, vol. 29, No. 1, Article 2, Publication date: Feb. 2011, in 45 pages.
Lin, “MR-Apriori: Association Rules Algorithm Based on MapReduce”, IEEE, 2014, pp. 141-144.
Meng et al., Efficient resource provisioning in compute clouds via VM multiplexing, Jun. 2010, 10 pages.
Merkel, “Docker: Lightweight Linux Containers for Consistent Development and Deployment”, Linux Journal, vol. 2014 Issue 239, Mar. 2014, XP055171140, 16 pages.
Monteil, Coupling profile and historical methods to predict execution time of parallel applications. Parallel and Cloud Computing, Jul. 2013, <hal-01228236, pp. 81-89.
Nakajima, J., et al., Optimizing Virtual Machines Using Hybrid Virtualization, SAC '11, Mar. 21-25, 2011, TaiChung, Taiwan, pp. 573-578.
Qian, H., and D. Medhi, et al., Estimating Optimal Cost of Allocating Virtualized Resources With Dynamic Demand, ITC 2011, Sep. 2011, pp. 320-321.
Ryden et al., “Nebula: Distributed Edge Cloud for Data-Intensive Computing”, IEEE, 2014, pp. 491-492.
Sakamoto, et al. “Platform for Web Services using Proxy Server”; Research Report from Information Processing Society, Mar. 22, 2002, vol. 2002, No. 31.
Search Query Report from IP.com, performed Dec. 2, 2020.
Search Query Report from IP.com, performed May 27, 2021.
Shim (computing), Wikipedia, https://en.wikipedia.org/w/index.php?title+Shim_(computing)&oldid+654971528, [retrieved on May 26, 2016], 2 pages.
Stack Overflow, Creating a database connection pool, Nov. 10, 2009, 4 pages.
Tan et al., Provisioning for large scale cloud computing services, Jun. 2012, 2 pages.
Tange, “GNU Parallel: The Command-Line Power Tool”, vol. 36, No. 1, Jan. 1, 1942, pp. 42-47.
Vaghani, S.B., Virtual Machine File System, ACM SIGOPS Operating Systems Review 44(4):57-70, Dec. 2010.
Vaquero, L., et al., Dynamically Scaling Applications in the cloud, ACM SIGCOMM Computer Communication Review 41(1):45-52, Jan. 2011.
Wang et al., “Improving utilization through dynamic VM resource allocation in hybrid cloud environment”, Parallel and Distributed V Systems (ICPADS), IEEE, Dec. 16, 2014-Dec. 19, 2014. Retrieved on Feb. 14, 2019, Retrieved from the internet: URL<https://ieeexplore.IEEE.org/stamp/stamp.jsp?tp=&arnumber=7097814, 8 pages.
Wikipedia “API” pages from date Apr. 7, 2015, retrieved using the WayBackMachine from https://web.archive.org/web/20150407191158/https://en wikipedia.org/wiki/Application_programming_interface.
Wikipedia List_of_HTTP status_codes web page, retrieved from https://en.wikipedia.org/wiki/List_of_HTTP status_codes, retrieved on Sep. 10, 2019.
Wikipedia Recursion web page from date Mar. 26, 2015, retrieved using the WayBackMachine, from https://web.archive.org/web/20150326230100/https://en.wikipedia.org/wiki/Recursion_(computer _science), 2015.
Wikipedia subroutine web page, retrieved from https://en.wikipedia.org/wiki/Subroutine, retrieved on Sep. 10, 2019.
Wood, Timothy, et al. “Cloud Net: dynamic pooling of cloud resources by live WAN migration of virtual machines.” ACM Sigplan Notices 46.7 (2011): 121-132. (Year: 2011).
Wu et al., HC-Midware: A Middleware to Enable High Performance Communication System Simulation in Heterogeneous Cloud, Association for Computing Machinery, Oct. 20-22, 2017, 10 pages.
Yamasaki et al. “Model-based resource selection for efficient virtual cluster deployment”, Virtualization Technology in Distributed Computing, ACM, Nov. 2007, pp. 1-7.
Yang, The Application of MapReduce in the Cloud Computing:, IEEE, 2011, pp. 154-156.
Yue et al., AC 2012-4107: Using Amazon EC2 in Computer and Network Security Lab Exercises: Design, Results, and Analysis, 2012, American Society for Engineering Education, Jun. 10, 2012.
Zhang et al., VMThunder: Fast Provisioning of Large-Scale Virtual Machine Clusters, IEEE Transactions on Parallel and Distributed Systems, vol. 25, No. 12, Dec. 2014, pp. 3328-3338.
Zheng, C., and D. Thain, Integrating Containers into Workflows: A Case Study Using Makeflow, Work Queue, and Docker, VTDC '15, Jun. 15, 2015, Portland, Oregon, pp. 31-38.
International Search Report and Written Opinion in PCT/US2015/052810 dated Dec. 17, 2015.
International Preliminary Report on Patentability in PCT/US2015/052810 dated Apr. 4, 2017.
Extended Search Report in European Application No. 15846932.0 dated May 3, 2018.
Office Action in Chinese Application No. 202110268031.5, dated Sep. 3, 2021.
Office Action in Canadian Application No. 2,962,633 dated May 21, 2020.
Office Action in Canadian Application No. 2,962,633 dated Jun. 18, 2021.
Office Action in European Application No. 19199402.9 dated Mar. 23, 2021.
International Search Report and Written Opinion in PCT/US2015/052838 dated Dec. 18, 2015.
International Preliminary Report on Patentability in PCT/US2015/052838 dated Apr. 4, 2017.
Extended Search Report in European Application No. 15847202.7 dated Sep. 9, 2018.
Extended Search Report in European Application No. 19199402.9 dated Mar. 6, 2020.
Office Action in Japanese Application No. 2017-516160 dated Jan. 15, 2018.
Notice of Allowance in Japanese Application No. 2017-516160 dated May 8, 2018.
Office Action in Canadian Application No. 2,962,631 dated May 19, 2020.
Office Action in Canadian Application No. 2,962,631 dated May 31, 2021.
Office Action in Indian Application No. 201717013356 dated Jan. 22, 2021.
Office Action in Japanese Application No. 2017-516168 dated Mar. 26, 2018.
International Search Report and Written Opinion in PCT/US2015/052833 dated Jan. 13, 2016.
International Preliminary Report on Patentability in PCT/US2015/052833 dated Apr. 4, 2017.
Extended Search Report in European Application No. 15846542.7 dated Aug. 27, 2018.
Office Action in Indian Application No. 201717019903 dated May 18, 2020.
International Search Report and Written Opinion in PCT/US2015/064071 dated Mar. 16, 2016.
International Preliminary Report on Patentability in PCT/US2015/064071 dated Jun. 6, 2017.
Office Action in Australian Application No. 2016215438 dated Feb. 26, 2018.
Notice of Allowance in Australian Application No. 2016215438 dated Nov. 19, 2018.
Office Action in Canadian Application No. 2,975,522 dated Jun. 5, 2018.
Notice of Allowance in Canadian Application No. 2,975,522 dated Mar. 13, 2020.
Office Action in Indian Application No. 201717027369 dated May 21, 2020.
International Search Report and Written Opinion in PCT/US2016/016211 dated Apr. 13, 2016.
International Preliminary Report on Patentability in PCT/US2016/016211 dated Aug. 17, 2017.
Office Action in Chinese Application No. 201680020768.2 dated May 14, 2021 in 23 pages.
Office Action in Chinese Application No. 201680020768.2 dated Sep. 24, 2021 in 20 pages.
First Examination Report for Indian Application No. 201717034806 dated Jun. 25, 2020.
International Search Report and Written Opinion in PCT/US2016/026514 dated Jun. 8, 2016.
International Preliminary Report on Patentability in PCT/US2016/026514 dated Oct. 10, 2017.
International Search Report and Written Opinion in PCT/US2016/026520 dated Jul. 5, 2016.
International Preliminary Report on Patentability in PCT/US2016/026520 dated Oct. 10, 2017,
Office Action in Chinese Application No. 2016800562398 dated Jun. 18, 2021.
International Search Report and Written Opinion in PCT/US2016/054774 dated Dec. 16, 2016.
Office Action in European Application No. 16781265.0 dated Jul. 13, 2020.
Office Action in Indian Application No. 201817013748 dated Nov. 20, 2020.
International Preliminary Report on Patentability in PCT/US2016/054774 dated Apr. 3, 2018.
Office Action in Chinese Application No. 201680072794X dated Jun. 22, 2021.
Office Action in European Application No. 16823419.3 dated Mar. 12, 2021.
International Search Report and Written Opinion in PCT/US2016/066997 dated Mar. 20, 2017.
International Preliminary Report on Patentability in PCT/US2016/066997 dated Jun. 26, 2018.
Office Action in Chinese Application No. 201780022789.2 dated Apr. 28, 2021.
Office Action in European Application No. 17776325.7 dated Apr. 12, 2021.
International Search Report and Written Opinion in PCT/US/2017/023564 dated Jun. 6, 2017.
International Preliminary Report on Patentability in PCT/US/2017/023564 dated Oct. 2, 2018.
Office Action in Chinese Application No. 2017800451968 dated May 26, 2021.
Office Action in European Application No. 17740533.9 dated May 4, 2021.
International Search Report and Written Opinion in PCT/US2017/040054 dated Sep. 21, 2017.
International Preliminary Report on Patentability in PCT/US2017/040054 dated Jan. 1, 2019.
International Search Report and Written Opinion in PCT/US2017/039514 dated Oct. 10, 2017.
International Preliminary Report on Patentability in PCT/US2017/039514 dated Jan. 1, 2019.
Extended European Search Report in application No. 17776325.7 dated Oct. 23, 2019.
Office Action in European Application No. 17743108.7 dated Jan. 14, 2020.
Office Action in European Application No. 17743108.7 dated Dec. 22, 2020.
International Search Report and Written Opinion dated Oct. 15, 2019 for International Application No. PCT/US2019/039246 in 16 pages.
International Preliminary Report on Patentability dated Dec. 29, 2020 for International Application No. PCT/US2019/039246 in 8 pages.
International Search Report for Application No. PCT/US2019/038520 dated Aug. 14, 2019.
International Preliminary Report on Patentability for Application No. PCT/US2019/038520 dated Dec. 29, 2020.
International Preliminary Report on Patentability and Written Opinion in PCT/US2019/053123 dated Mar. 23, 2021.
International Search Report and Written Opinion in PCT/US2019/053123 dated Jan. 7, 2020.
International Search Report for Application No. PCT/US2019/065365 dated Mar. 19, 2020.
International Preliminary Report on Patentability for Application No. PCT/US2019/065365 dated Jun. 8, 2021.
International Search Report for Application No. PCT/US2020/039996 dated Oct. 8, 2020.
International Search Report for Application No. PCT/US2020/062060 dated Mar. 5, 2021.

Streaming analytics using a serverless compute system

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

US

International Classifications

Abstract

Description

Claims

US Referenced Citations (850)

Foreign Referenced Citations (66)

Non-Patent Literature Citations (134)