This disclosure relates in general to the field of computing and, more particularly, to estimating model parameters for automatic deployment of scalable micro services.
Cloud computing aggregates physical and virtual compute, storage, and network resources in the “cloud” and offers users many ways to utilize the resources. One kind of product leveraging cloud computing is called Software-as-a-Service (SaaS). Software vendors can acquire resources in the cloud (e.g., renting and/or building their own) to run software applications for their customers. The cloud having physical hardware resources would host the software applications to which customers would have access. Software applications that can be offered using SaaS can include financial services application, gaming application, supply chain application, inventory management application, data management application, talent acquisition application, etc. Users or customers can request an instance of a software application from the software vendor. The software vendor can in turn instantiate the software application on the cloud. The customer would then be able to use and access the software application that he/she requested.
To provide a more complete understanding of the present disclosure and features and advantages thereof, reference is made to the following description, taken in conjunction with the accompanying figures, wherein like reference numerals represent like parts, in which:
One aspect of the disclosure relates to, among other things, a method for optimizing and provisioning a software-as-a-service (SaaS). The method includes determining a graph comprising interconnected stages for the SaaS, wherein each stage has a replication factor and one or more metrics that are associated with one or more service level objectives of the SaaS, determining a first replication factor associated with a first one of the stages which meets a first service level objective of the SaaS, adjusting the first replication factor associated with the first one of the stage based on the determined first replication factor, and provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage.
In other aspects, apparatuses comprising means for carrying out one or more of the method steps are envisioned by the disclosure. As will be appreciated by one skilled in the art, aspects of the disclosure, in particular the functionality associated with modelling and deploying scalable micro services herein, may be embodied as a system, a method or a computer program product. Accordingly, aspects of the disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Functions described in this disclosure may be implemented as an algorithm executed by a processor, e.g., a microprocessor, of a computer. Furthermore, aspects of the disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied, e.g., stored, thereon.
A Software-as-a-Service (SaaS) offering has certain service level objectives (SLO) such as number of subscribers (or customers), number of requests, and volume of traffic. Furthermore, SaaS may have certain service level agreements (SLAs) with the subscribers that defines a commitment for aspects of the service such as quality, availability, etc. Increasingly, a SaaS service is composed of multiple micro-services which are interconnected to each other as different parts of a software application. Micro-services enables smaller, light weight processes to be used, which can offer benefits such as flexibility and scalability than other architectures. Micro-services can be replicated to increase throughput and efficiency.
Auto-scaling of an existing/running SaaS is not uncommon in cloud-based software. In auto-scaling, constant monitoring is required and based on certain monitored metrics (such as processor usage and memory usage), newer instances are automatically launched (and existing instances removed). The problem of being able to initially deploy a micro-services based SaaS optimally with the necessary scale numbers has yet to be addressed.
When deploying a SaaS onto the cloud, it is advantageous to determine the set of micro services that would meet SLOs and SLAs. Specifically, a solution would intelligently deploy a micro services based SaaS with necessary scale or replication numbers so that the SLAs and SLOs of the overall SaaS offering is met. A method is implemented to determine an initial installation of a SaaS that would meet the SLOs and SLAs, starting from a SaaS model provided to a SaaS manager.
A SaaS deployment is modeled as a graph with nodes representing the constituent stages and edges representing the flow of data through the stages.
The SaaS Manager 104 has corresponding SLO's and SLA's 108. Bottlenecks can greatly affect the ability to meet the SLO's and SLA's. For simplicity, embodiments described herein may reference them interchangeably, since the embodiments are equally applicable to meeting both SLOs and SLAs. When a SaaS service is designed, there are some overall service level objectives. Some typical SLOs are:
Because the SaaS is deployed using micro services, the stages may be scalable or replicated easily. However, determining how to scale the stages optimally to meet the SLOs is not trivial. In other words, when a SaaS is deployed, it is not clear how to resource (e.g., replicate) each of its stages (and thus each of the sub-stages of the stages) such that the overall SLOs of the SaaS can be satisfied.
Referring back to
However, if the stage 306 can be replicated, the SLO can be met, under the assumption that each replicated copy of stage 306 can independently handle the data payload, the SLO can be maintained.
A SaaS is represented as a model. The model has a graph comprising interconnected stages for the SaaS, as the nodes of the graph. Nodes represent the constituent stages and edges can represent the flow of data through the stages. Each node has one or more metrics that can, either directly or in combination with other metrics, translate to one or more SLO of the overall SaaS. In other words, the metrics are associated with one or more service level objectives of the SaaS, and the metrics provide information whether one or more service level objectives can be met. A stage can have sub-stages therein. Each stage also has a replication factor, which specifies the number of copies the stage is replicated. Furthermore, each stage has a maximum replication factor, which specifies the maximum number of copies the stage can be replicated.
Model can be written in a suitable modeling language such as Extensible Markup Language (XML) or YAML or in other specification formats such as Topology and Orchestration Specification for Cloud Applications (TOSCA) (used for web services running in the cloud) or OpenStack Heat. For easier understanding, the following is a model written in YAML.
In 504, to optimize the SaaS installation, the optimizer can determine a first replication factor associated with a first one of the stages which meets a first service level objective of the SaaS. In some embodiments, a replication factor of a given stage is adjusted, and the SLO of the SaaS is evaluated based on the adjusted replication factor to determine whether the SLO of the SaaS can be met. Adjusting the replication factor and evaluating the SLO can include determining whether the first service level is met based on the one or more metrics and the replication factor associated with each stage in the graph. When adjusting the replication factor for a given stage, the SLO can be evaluated based on the whole SaaS, i.e., all of the stages.
The following describes an exemplary method for determining replication factors that would meet an SLO relating to maximum throughput. Most SLOs can be handled in a similar fashion when it comes to calculating the dependency on the constituent stages. Suppose T is the maximum throughput that the SaaS needs to support. The SaaS can have n stages and each stage can support a maximum throughput of ti. This means that the maximum throughput supported by the SaaS would be:
To meet the SLO:
Tmax≥T
Put in words, each stage has a metric ti which specifies the stage's maximum throughput. The maximum throughput of the SaaS would be the minimum of the metrics associated with the stages specifying the maximum throughput of the stages. To meet the overall SLO T (the maximum throughput that the SaaS needs to support), the minimum has to be greater than T.
Suppose there is a stage i such that ti<T. If that stage is not scalable (as specified in the model as the maximum replication factor), the SaaS SLO for throughput cannot be achieved. However, if the stages are scalable, any one or more of the stages can be replicated to increase throughput. Micro services can be scaled horizontally, i.e., parallel micro services, leading to linear increase in performance (albeit at the cost of higher complexity of managing the multiple instances). To meet the SLA, the method can determine a replication factor of stage i, ri as the smallest positive integer such that:
tij is the throughput supported by the jth instance of the stage. Determining replication factor and adjusting the replication factor can help meet the SLO. If identical copies/instances cannot be launched, the capacity of each copy can be considered. However, more often than not, all the instances of the same stage would have equal capability and hence previous equation can reduce to: riti≥T.
In some embodiments, each stage has a maximum replication factor (“max_replication_factor”). The maximum replication factor limits how many instances a stage can be replicated. Determining the first replication factor can include checking whether the first replication factor exceeds the maximum replication factor. If the calculated replication factor is higher than the max_replication_factor specified in the model, it means that the target SLA cannot be achieved.
In 506, the optimizer can adjust the first replication factor associated with the first one of the stage based on the determined first replication factor. In 508, a provisioner (e.g., provisioner 114 of
The above example illustrates determining and adjusting the replication factor for a stage for a single SLO. In practice, there may be multiple SLO metrics, and the method is performed a number of SLOs, and the maximum of all the determined replication factors of a given stage for all the SLOs is considered as the replication factor for the given stage. In some embodiments, the method would further include determining one or more replication factors associated with the first one of the stages which meets one or more further service level objectives. Adjusting the first replication factor associated with the first one of the stages comprises adjusting the first one of the stages based on a maximum of the determined replication factors. For example, there might be 2 instances needed (determined replication factor of 2) to support the number of users (a first SLO) but 3 instances required (determined replication factor of 3) to support the required throughput (a second SLO). In such a scenario, the replication factor should be considered as 3, the maximum of the two determined replication factors (for meeting the first SLO and the second SLO respectively).
Besides computing the replication factor for one stage for a given SLO, the method is to be performed on every other stage to determine the appropriate replication factor to meet the SLO. In other words, besides determining a first replication factor for a given stage, the method would iterate through other stages in the graph to determine further replication factors for a given SLO. In some embodiments, the method would further include determining a second replication factor associated with a second one of the stages which meets the first service level objective of the SaaS, and adjusting the second replication factor associated with the second one of the stage based on the determined second replication factor. For example, in a pipeline with 3 stages and 2 SLO metrics, a total of 6 replication factors may have to be computed, i.e., 2 replication factors at each stage. The maximum replication factor from each stage would be selected as the final replication factor (3 replication factors, one for each stage).
Whenever stages have sub-stages, the computation of the replication factor for sub-stages may have similar computation to figure out the replication factor for each of the individual sub-stages so that the desired SLO can be achieved for the stage.
The following is an example of a SaaS model and the installation method implemented in pseudocode, which illustrates how to determine replication factors for all the stages and all the SLOs.
The installation method and its implementation are not trivial. First, there are certain metrics that cannot be handled in a straightforward manner. One example of such metric is delay. End-to-end delay keeps increasing with every stage. In order to keep delay within SLA bounds, either a single stage can be replicated or multiple stages can be replicated. It is not clear which one should be preferred. A maximum replication factor being set for each stage can prevent a single stage from replicating without limit to bring down delay. Rather, having maximum replication factors for each stage would enable the method to consider other stages for replication.
In some cases, the data flow deviates from an ideal pipeline (seen in many examples herein) and a single stage can forward data to two different stages, each stage performing different processing. For example, a processing stage can forward logs data to one stage and metrics data to another stage. One way to handle this is to include a split in the model itself (this split can be empirically determined or can be hard coded into the underlying model). The method can still be applied to a SaaS model with this property.
When the target SLO cannot be achieved, the installer method may stop with an error message. In some cases the installer may continue to install the SaaS according to the determined replication factors which can achieve only portion of the target SLO.
Whenever, any instance is replicated, it may be beneficial to launch a load balancer to evenly distribute the incoming load among the multiple instances. In other words, the method of optimizing and provisioning the SaaS may include provisioning a load balancer in front of a stage whose replication factor was increased to meet service level objectives.
In some cases, metrics for a stage within a graph may not be available. For instance, some metrics may depend on the overall graph and dynamics of the stages. The method for installation may further include determining an estimated metric if the metric is not predefined. One example of such metric is queue length.
The entire SaaS having the various queues can then modeled as a Jackson Network, i.e., a network of M/M/1 FIFO queues where jobs enter and exit the network (as opposed to looping in the network). Properties of Jackson Networks can be used to estimate the queue lengths in each stage (and sub-stage(s)) of the SaaS, i.e., metrics for these stages. Based on the estimated metric, it is possible to determine replication factors which can meet a given SLO associated with that metric. For instance, the installation method can then calculate the replication factor for each stage to keep the queue length of each stage under some predefined number. For example, if the desired queue length for a stage is 50 (as an example of an SLO) whereas the calculated/estimated queue length (as an example of an estimated metric) is 100 (or is unstable, e.g., the arrival rate at the queue is higher than the service rate of the queue), a replication factor of 2 can ensure an expected queue length of 50 in each of the two instances of the stage.
The memory elements 804 may include one or more physical memory devices such as, for example, local memory 808 and one or more bulk storage devices 810. The local memory may refer to random access memory or other non-persistent memory device(s) generally used during actual execution of the program code. A bulk storage device may be implemented as a hard drive or other persistent data storage device. The processing system 800 may also include one or more cache memories (not shown) that provide temporary storage of at least some program code in order to reduce the number of times program code must be retrieved from the bulk storage device 810 during execution.
Input/output (I/O) devices depicted as an input device 812 and an output device 814 optionally can be coupled to the data processing system. User (machines) accessing the application implemented with the SaaS would typically have such I/O devices. Examples of input devices may include, but are not limited to, a keyboard, a pointing device such as a mouse, or the like. Examples of output devices may include, but are not limited to, a monitor or a display, speakers, or the like. Input and/or output devices may be coupled to the data processing system either directly or through intervening I/O controllers. In an embodiment, the input and the output devices may be implemented as a combined input/output device (illustrated in
A network adapter 816 may also be coupled to the data processing system to enable it to become coupled to other systems, computer systems, remote network devices, and/or remote storage devices through intervening private or public networks. The network adapter may comprise a data receiver for receiving data that is transmitted by said systems, devices and/or networks to the data processing system 800, and a data transmitter for transmitting data from the data processing system 800 to said systems, devices and/or networks. Modems, cable modems, and Ethernet cards are examples of different types of network adapter that may be used with the data processing system 800.
As pictured in
Persons skilled in the art will recognize that while the elements 802-818 are shown in
Example 1 is a method for optimizing and provisioning a software-as-a-service (SaaS), the method comprising: determining a graph comprising interconnected stages for the SaaS, wherein each stage has a replication factor and one or more metrics that are associated with one or more service level objectives of the SaaS; determining a first replication factor associated with a first one of the stages which meets a first service level objective of the SaaS; adjusting the first replication factor associated with the first one of the stage based on the determined first replication factor; and provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage.
In Example 2, the method of Example 1 can further include: each stage having a maximum replication factor; and determining the first replication factor comprising checking whether the first replication factor exceeds the maximum replication factor.
In Example 3, the method of Example 1 or 2 can further include: determining a second replication factor associated with a second one of the stages which meets the first service level objective of the SaaS; and adjusting the second replication factor associated with the second one of the stage based on the determined second replication factor.
In Example 4, the method of any one of Examples 1-3 can further include determining the first replication factor associated with the first one of the stages which meets the first service level objective comprising determining whether the first service level is met based on the one or more metrics and the replication factor associated with each stage in the graph.
In Example 5, the method of any one of Examples 1-4 can further include: determining one or more replication factors associated with the first one of the stages which meets one or more further service level objectives; and wherein adjusting the first replication factor associated with the first one of the stages comprises adjusting the first one of the stages based on a maximum of the determined replication factors.
In Example 6, the method of any one of Examples 1-5 can further include provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage comprising provisioning a load balancer in front of a stage whose replication factor was increased to meet a service level objective.
In Example 7, the method of any one of Examples 1-6 can further include determining a first metric associated with each stage by modeling each stage as a first in first out queue with exponentially distributed service time, wherein the first metric is queue length.
Example 8 is a system comprising: at least one memory element; at least one processor coupled to the at least one memory element; and a software-as-a-service (SaaS) optimizer that when executed by the at least one processor is configured to: determine a graph comprising interconnected stages for a SaaS, wherein each stage has a replication factor and one or more metrics that are associated with one or more service level objectives of the SaaS; determine a first replication factor associated with a first one of the stages which meets a first service level objective of the SaaS; and adjust the first replication factor associated with the first one of the stage based on the determined first replication factor; and a SaaS provisioner that when executed by the at least one processor is configured to provision the SaaS onto networked computing resources based on the graph and replication factors associated with each stage.
In Example 9, the system of Example 8 can further include: each stage having a maximum replication factor; and determining the first replication factor comprising checking whether the first replication factor exceeds the maximum replication factor.
In Example 10, the system of Example 8 or 9 can further include the SaaS optimizer being further configured to: determine a second replication factor associated with a second one of the stages which meets the first service level objective of the SaaS; and adjust the second replication factor associated with the second one of the stage based on the determined second replication factor.
In Example 11, the system of any one of Examples 8-10 can further include determining the first replication factor associated with the first one of the stages which meets the first service level objective comprising determining whether the first service level is met based on the one or more metrics and the replication factor associated with each stage in the graph.
In Example 12, the system of any one of Examples 8-11 can further include the SaaS optimizer being further configured to: determine one or more replication factors associated with the first one of the stages which meets one or more further service level objectives; and wherein adjusting the first replication factor associated with the first one of the stages comprises adjusting the first one of the stages based on a maximum of the determined replication factors.
In Example 13, the system of any one of Examples 8-12 can further include provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage comprising provisioning a load balancer in front of a stage whose replication factor was increased to meet a service level objective.
In Example 14, the system of any one of Examples 8-13 can further include the SaaS optimizer being further configured to: determine a first metric associated with each stage by modeling each stage as a first in first out queue with exponentially distributed service time, wherein the first metric is queue length.
Example 15 is a computer-readable non-transitory medium comprising one or more instructions, for optimizing and provisioning a software-as-a-service (SaaS), that when executed on a processor configure the processor to perform one or more operations comprising: determining a graph comprising interconnected stages for the SaaS, wherein each stage has a replication factor and one or more metrics that are associated with one or more service level objectives of the SaaS; determining a first replication factor associated with a first one of the stages which meets a first service level objective of the SaaS; adjusting the first replication factor associated with the first one of the stage based on the determined first replication factor; and provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage.
In Example 16, the computer-readable non-transitory medium of Example 15 can further include: each stage having a maximum replication factor; and determining the first replication factor comprising checking whether the first replication factor exceeds the maximum replication factor.
In Example 17, the computer-readable non-transitory medium of Example 15 or 16 can further include: determining a second replication factor associated with a second one of the stages which meets the first service level objective of the SaaS; and adjusting the second replication factor associated with the second one of the stage based on the determined second replication factor.
In Example 18, the computer-readable non-transitory medium of any one of Examples 15-17 can further include determining the first replication factor associated with the first one of the stages which meets the first service level objective comprising determining whether the first service level is met based on the one or more metrics and the replication factor associated with each stage in the graph.
In Example 19, the computer-readable non-transitory medium of any one of Examples 15-18 can further include the operations further comprising: determining one or more replication factors associated with the first one of the stages which meets one or more further service level objectives; and wherein adjusting the first replication factor associated with the first one of the stages comprises adjusting the first one of the stages based on a maximum of the determined replication factors.
In Example 20, the computer-readable non-transitory medium of any one of Examples 15-19 can further include provisioning the SaaS onto networked computing resources based on the graph and replication factors associated with each stage comprising provisioning a load balancer in front of a stage whose replication factor was increased to meet a service level objective.
In Example 21, the computer-readable non-transitory medium of any one of Examples 15-20 can further include the operations further comprising determining a first metric associated with each stage by modeling each stage as a first in first out queue with exponentially distributed service time, wherein the first metric is queue length.
Example 20 is an apparatus comprising means for implementing and/or carrying out any one of the methods in Examples 1-7.
Within the context of the disclosure, the cloud includes a network used herein represents a series of points, nodes, or network elements of interconnected communication paths for receiving and transmitting packets of information that propagate through a communication system. A network offers communicative interface between sources and/or hosts, and may be any local area network (LAN), wireless local area network (WLAN), metropolitan area network (MAN), Intranet, Extranet, Internet, WAN, virtual private network (VPN), or any other appropriate architecture or system that facilitates communications in a network environment depending on the network topology. A network can comprise any number of hardware or software elements coupled to (and in communication with) each other through a communications medium.
As used herein in this Specification, the term ‘network element’ or ‘node’ in the cloud is meant to encompass any of the aforementioned elements, as well as servers (physical or virtually implemented on physical hardware), machines (physical or virtually implemented on physical hardware), end user devices, routers, switches, cable boxes, gateways, bridges, loadbalancers, firewalls, inline service nodes, proxies, processors, modules, or any other suitable device, component, element, proprietary appliance, or object operable to exchange, receive, and transmit information in a network environment. These network elements may include any suitable hardware, software, components, modules, interfaces, or objects that facilitate the disclosed operations. This may be inclusive of appropriate algorithms and communication protocols that allow for the effective exchange of data or information.
In one implementation, SaaS manager described herein may include software to achieve (or to foster) the functions discussed herein for optimizing and provisioning a SaaS (also referenced herein as installation) where the software is executed on one or more processors to carry out the functions. This could include the implementation of instances of an optimizer, provisioner, and/or any other suitable element that would foster the activities discussed herein. Additionally, each of these elements can have an internal structure (e.g., a processor, a memory element, etc.) to facilitate some of the operations described herein. Exemplary internal structure includes elements shown in data processing system in
In certain example implementations, the functions outlined herein may be implemented by logic encoded in one or more non-transitory, tangible media (e.g., embedded logic provided in an application specific integrated circuit [ASIC], digital signal processor [DSP] instructions, software [potentially inclusive of object code and source code] to be executed by one or more processors, or other similar machine, etc.). In some of these instances, one or more memory elements can store data used for the operations described herein. This includes the memory element being able to store instructions (e.g., software, code, etc.) that are executed to carry out the activities described in this Specification. The memory element is further configured to store information such as graph definitions, metrics, SLOs/SLAs, and replication factors disclosed herein. The processor can execute any type of instructions associated with the data to achieve the operations detailed herein in this Specification. In one example, the processor could transform an element or an article (e.g., data) from one state or thing to another state or thing. In another example, the activities outlined herein may be implemented with fixed logic or programmable logic (e.g., software/computer instructions executed by the processor) and the elements identified herein could be some type of a programmable processor, programmable digital logic (e.g., a field programmable gate array [FPGA], an erasable programmable read only memory (EPROM), an electrically erasable programmable ROM (EEPROM)) or an ASIC that includes digital logic, software, code, electronic instructions, or any suitable combination thereof.
Any of these elements (e.g., the network elements, etc.) can include memory elements for storing information to be used in achieving the optimization functions, as outlined herein. Additionally, each of these devices may include a processor that can execute software or an algorithm to perform the optimization activities as discussed in this Specification. These devices may further keep information in any suitable memory element [random access memory (RAM), ROM, EPROM, EEPROM, ASIC, etc.], software, hardware, or in any other suitable component, device, element, or object where appropriate and based on particular needs. Any of the memory items discussed herein should be construed as being encompassed within the broad term ‘memory element.’ Similarly, any of the potential processing elements, modules, and machines described in this Specification should be construed as being encompassed within the broad term ‘processor.’ Each of the network elements can also include suitable interfaces for receiving, transmitting, and/or otherwise communicating data or information in a network environment.
Additionally, it should be noted that with the examples provided above, interaction may be described in terms of two, three, or four network elements. However, this has been done for purposes of clarity and example only. In certain cases, it may be easier to describe one or more of the functionalities of a given set of flows by only referencing a limited number of network elements. It should be appreciated that the systems described herein are readily scalable and, further, can accommodate a large number of components, as well as more complicated/sophisticated arrangements and configurations. Accordingly, the examples provided should not limit the scope or inhibit the broad techniques of SaaS optimization and provisioning, as potentially applied to a myriad of other architectures.
It is also important to note that the parts of the flow diagram in the
The term “system” is used generically herein to describe any number of components, elements, sub-systems, devices, packet switch elements, packet switches, routers, networks, computer and/or communication devices or mechanisms, or combinations of components thereof. The term “computer” is used generically herein to describe any number of computers, including, but not limited to personal computers, embedded processing elements and systems, control logic, ASICs, chips, workstations, mainframes, etc. The term “processing element” is used generically herein to describe any type of processing mechanism or device, such as a processor, ASIC, field programmable gate array, computer, etc. The term “device” is used generically herein to describe any type of mechanism, including a computer or system or component thereof. The terms “task” and “process” are used generically herein to describe any type of running program, including, but not limited to a computer process, task, thread, executing application, operating system, user process, device driver, native code, machine or other language, etc., and can be interactive and/or non-interactive, executing locally and/or remotely, executing in foreground and/or background, executing in the user and/or operating system address spaces, a routine of a library and/or standalone application, and is not limited to any particular memory partitioning technique. The steps, connections, and processing of signals and information illustrated in the FIGURES, including, but not limited to any block and flow diagrams and message sequence charts, may typically be performed in the same or in a different serial or parallel ordering and/or by different components and/or processes, threads, etc., and/or over different connections and be combined with other functions in other embodiments, unless this disables the embodiment or a sequence is explicitly or implicitly required (e.g., for a sequence of read the value, process the value—the value must be obtained prior to processing it, although some of the associated processing may be performed prior to, concurrently with, and/or after the read operation). Furthermore, the term “identify” is used generically to describe any manner or mechanism for directly or indirectly ascertaining something, which may include, but is not limited to receiving, retrieving from memory, determining, defining, calculating, generating, etc.
Moreover, the terms “network” and “communications mechanism” are used generically herein to describe one or more networks, communications mediums or communications systems, including, but not limited to the Internet, private or public telephone, cellular, wireless, satellite, cable, local area, metropolitan area and/or wide area networks, a cable, electrical connection, bus, etc., and internal communications mechanisms such as message passing, interprocess communications, shared memory, etc. The term “message” is used generically herein to describe a piece of information which may or may not be, but is typically communicated via one or more communication mechanisms of any type.
Numerous other changes, substitutions, variations, alterations, and modifications may be ascertained to one skilled in the art and it is intended that the present disclosure encompass all such changes, substitutions, variations, alterations, and modifications as falling within the scope of the appended claims. In order to assist the United States Patent and Trademark Office (USPTO) and, additionally, any readers of any patent issued on this application in interpreting the claims appended hereto, Applicant wishes to note that the Applicant: (a) does not intend any of the appended claims to invoke paragraph six (6) of 35 U.S.C. section 112 as it exists on the date of the filing hereof unless the words “means for” or “step for” are specifically used in the particular claims; and (b) does not intend, by any statement in the specification, to limit this disclosure in any way that is not otherwise reflected in the appended claims.
One or more advantages mentioned herein does not in any way suggest that any one of the embodiments necessarily provides all the described advantages or that all the embodiments of the present disclosure necessarily provide any one of the described advantages.
Number | Name | Date | Kind |
---|---|---|---|
5812773 | Norin | Sep 1998 | A |
5889896 | Meshinsky et al. | Mar 1999 | A |
6108782 | Fletcher et al. | Aug 2000 | A |
6178453 | Mattaway et al. | Jan 2001 | B1 |
6298153 | Oishi | Oct 2001 | B1 |
6343290 | Cossins et al. | Jan 2002 | B1 |
6643260 | Kloth et al. | Nov 2003 | B1 |
6683873 | Kwok et al. | Jan 2004 | B1 |
6721804 | Rubin et al. | Apr 2004 | B1 |
6733449 | Krishnamurthy et al. | May 2004 | B1 |
6735631 | Oehrke et al. | May 2004 | B1 |
6996615 | McGuire | Feb 2006 | B1 |
7054930 | Cheriton | May 2006 | B1 |
7058706 | Lyer et al. | Jun 2006 | B1 |
7062571 | Dale et al. | Jun 2006 | B1 |
7111177 | Chauvel et al. | Sep 2006 | B1 |
7212490 | Kao et al. | May 2007 | B1 |
7277948 | Igarashi et al. | Oct 2007 | B2 |
7313667 | Pullela et al. | Dec 2007 | B1 |
7379846 | Williams et al. | May 2008 | B1 |
7480672 | Hahn et al. | Jan 2009 | B2 |
7496043 | Leong et al. | Feb 2009 | B1 |
7536476 | Alleyne | May 2009 | B1 |
7567504 | Darling et al. | Jul 2009 | B2 |
7583665 | Duncan et al. | Sep 2009 | B1 |
7606147 | Luft et al. | Oct 2009 | B2 |
7644437 | Volpano | Jan 2010 | B2 |
7647594 | Togawa | Jan 2010 | B2 |
7773510 | Back et al. | Aug 2010 | B2 |
7808897 | Mehta et al. | Oct 2010 | B1 |
7881957 | Cohen et al. | Feb 2011 | B1 |
7917647 | Cooper et al. | Mar 2011 | B2 |
8010598 | Tanimoto | Aug 2011 | B2 |
8028071 | Mahalingam et al. | Sep 2011 | B1 |
8041714 | Aymeloglu et al. | Oct 2011 | B2 |
8121117 | Amdahl et al. | Feb 2012 | B1 |
8171415 | Appleyard et al. | May 2012 | B2 |
8234377 | Cohn | Jul 2012 | B2 |
8244559 | Horvitz et al. | Aug 2012 | B2 |
8250215 | Stienhans et al. | Aug 2012 | B2 |
8280880 | Aymeloglu et al. | Oct 2012 | B1 |
8284664 | Aybay et al. | Oct 2012 | B1 |
8301746 | Head et al. | Oct 2012 | B2 |
8345692 | Smith | Jan 2013 | B2 |
8406141 | Couturier et al. | Mar 2013 | B1 |
8407413 | Yucel et al. | Mar 2013 | B1 |
8448171 | Donnellan et al. | May 2013 | B2 |
8477610 | Zuo et al. | Jul 2013 | B2 |
8495356 | Ashok et al. | Jul 2013 | B2 |
8495725 | Ahn | Jul 2013 | B2 |
8510469 | Portolani | Aug 2013 | B2 |
8514868 | Hill | Aug 2013 | B2 |
8532108 | Li et al. | Sep 2013 | B2 |
8533687 | Greifeneder et al. | Sep 2013 | B1 |
8547974 | Guruswamy et al. | Oct 2013 | B1 |
8560639 | Murphy et al. | Oct 2013 | B2 |
8560663 | Baucke et al. | Oct 2013 | B2 |
8589543 | Dutta et al. | Nov 2013 | B2 |
8590050 | Nagpal et al. | Nov 2013 | B2 |
8611356 | Yu et al. | Dec 2013 | B2 |
8612625 | Andries et al. | Dec 2013 | B2 |
8630291 | Shaffer et al. | Jan 2014 | B2 |
8639787 | Lagergren et al. | Jan 2014 | B2 |
8656024 | Krishnan et al. | Feb 2014 | B2 |
8660129 | Brendel et al. | Feb 2014 | B1 |
8719804 | Jain | May 2014 | B2 |
8775576 | Hebert et al. | Jul 2014 | B2 |
8797867 | Chen et al. | Aug 2014 | B1 |
8805951 | Faibish et al. | Aug 2014 | B1 |
8850002 | Dickinson et al. | Sep 2014 | B1 |
8850182 | Fritz et al. | Sep 2014 | B1 |
8856339 | Mestery et al. | Oct 2014 | B2 |
8909928 | Ahmad et al. | Dec 2014 | B2 |
8918510 | Gmach et al. | Dec 2014 | B2 |
8924720 | Raghuram et al. | Dec 2014 | B2 |
8930747 | Levijarvi et al. | Jan 2015 | B2 |
8938775 | Roth et al. | Jan 2015 | B1 |
8959526 | Kansal et al. | Feb 2015 | B2 |
8977754 | Curry, Jr. et al. | Mar 2015 | B2 |
9009697 | Breiter et al. | Apr 2015 | B2 |
9015324 | Jackson | Apr 2015 | B2 |
9043439 | Bicket et al. | May 2015 | B2 |
9049115 | Rajendran et al. | Jun 2015 | B2 |
9063789 | Beaty et al. | Jun 2015 | B2 |
9065727 | Liu et al. | Jun 2015 | B1 |
9075649 | Bushman et al. | Jul 2015 | B1 |
9130846 | Szabo et al. | Sep 2015 | B1 |
9164795 | Vincent | Oct 2015 | B1 |
9167050 | Durazzo et al. | Oct 2015 | B2 |
9201701 | Boldyrev et al. | Dec 2015 | B2 |
9201704 | Chang et al. | Dec 2015 | B2 |
9203784 | Chang et al. | Dec 2015 | B2 |
9223634 | Chang et al. | Dec 2015 | B2 |
9244776 | Koza et al. | Jan 2016 | B2 |
9251114 | Ancin et al. | Feb 2016 | B1 |
9264478 | Hon et al. | Feb 2016 | B2 |
9294408 | Dickinson et al. | Mar 2016 | B1 |
9313048 | Chang et al. | Apr 2016 | B2 |
9361192 | Smith et al. | Jun 2016 | B2 |
9379982 | Krishna et al. | Jun 2016 | B1 |
9380075 | He et al. | Jun 2016 | B2 |
9432245 | Sorenson, III et al. | Aug 2016 | B1 |
9432294 | Sharma et al. | Aug 2016 | B1 |
9444744 | Sharma et al. | Sep 2016 | B1 |
9473365 | Melander et al. | Oct 2016 | B2 |
9503530 | Niedzielski | Nov 2016 | B1 |
9558078 | Farlee et al. | Jan 2017 | B2 |
9571570 | Mutnuru | Feb 2017 | B1 |
9613078 | Vermeulen et al. | Apr 2017 | B2 |
9628471 | Sundaram et al. | Apr 2017 | B1 |
9658876 | Chang et al. | May 2017 | B2 |
9692802 | Bicket et al. | Jun 2017 | B2 |
9755858 | Bagepalli et al. | Sep 2017 | B2 |
10097431 | Moniz | Oct 2018 | B1 |
10171371 | Anwar | Jan 2019 | B2 |
10250455 | Moniz | Apr 2019 | B1 |
10372518 | O'Kennedy | Aug 2019 | B2 |
10389596 | Strobel | Aug 2019 | B2 |
20010055303 | Horton et al. | Dec 2001 | A1 |
20020073337 | Ioele et al. | Jun 2002 | A1 |
20020143928 | Maltz et al. | Oct 2002 | A1 |
20020166117 | Abrams et al. | Nov 2002 | A1 |
20020174216 | Shorey et al. | Nov 2002 | A1 |
20030018591 | Komisky | Jan 2003 | A1 |
20030056001 | Mate et al. | Mar 2003 | A1 |
20030228585 | Inoko et al. | Dec 2003 | A1 |
20040004941 | Malan et al. | Jan 2004 | A1 |
20040034702 | He | Feb 2004 | A1 |
20040088542 | Daude et al. | May 2004 | A1 |
20040095237 | Chen et al. | May 2004 | A1 |
20040131059 | Ayyakad et al. | Jul 2004 | A1 |
20040197079 | Latvala et al. | Oct 2004 | A1 |
20040264481 | Darling et al. | Dec 2004 | A1 |
20050060418 | Sorokopud | Mar 2005 | A1 |
20050125424 | Herriott et al. | Jun 2005 | A1 |
20060013134 | Neuse | Jan 2006 | A1 |
20060062187 | Rune | Mar 2006 | A1 |
20060104286 | Cheriton | May 2006 | A1 |
20060126665 | Ward et al. | Jun 2006 | A1 |
20060146825 | Hofstaedter et al. | Jul 2006 | A1 |
20060155875 | Cheriton | Jul 2006 | A1 |
20060168338 | Bruegl et al. | Jul 2006 | A1 |
20060195896 | Fulp | Aug 2006 | A1 |
20060233106 | Achlioptas et al. | Oct 2006 | A1 |
20070174663 | Crawford et al. | Jul 2007 | A1 |
20070223487 | Kajekar et al. | Sep 2007 | A1 |
20070242830 | Conrado et al. | Oct 2007 | A1 |
20080005293 | Bhargava et al. | Jan 2008 | A1 |
20080080524 | Tsushima et al. | Apr 2008 | A1 |
20080084880 | Dharwadkar | Apr 2008 | A1 |
20080165778 | Ertemalp | Jul 2008 | A1 |
20080198752 | Fan et al. | Aug 2008 | A1 |
20080198858 | Townsley et al. | Aug 2008 | A1 |
20080201711 | Amir Husain | Aug 2008 | A1 |
20080235755 | Blaisdell et al. | Sep 2008 | A1 |
20090006527 | Gingell, Jr. et al. | Jan 2009 | A1 |
20090019367 | Cavagnari et al. | Jan 2009 | A1 |
20090031312 | Mausolf et al. | Jan 2009 | A1 |
20090083183 | Rao et al. | Mar 2009 | A1 |
20090138763 | Arnold | May 2009 | A1 |
20090177775 | Radia et al. | Jul 2009 | A1 |
20090178058 | Stillwell, III et al. | Jul 2009 | A1 |
20090182874 | Morford et al. | Jul 2009 | A1 |
20090265468 | Annambhotla et al. | Oct 2009 | A1 |
20090265753 | Anderson et al. | Oct 2009 | A1 |
20090293056 | Ferris | Nov 2009 | A1 |
20090300608 | Ferris et al. | Dec 2009 | A1 |
20090313562 | Appleyard et al. | Dec 2009 | A1 |
20090323706 | Germain et al. | Dec 2009 | A1 |
20090328031 | Pouyadou et al. | Dec 2009 | A1 |
20100036903 | Ahmad et al. | Feb 2010 | A1 |
20100042720 | Stienhans et al. | Feb 2010 | A1 |
20100061250 | Nugent | Mar 2010 | A1 |
20100076733 | Kumar | Mar 2010 | A1 |
20100115341 | Baker et al. | May 2010 | A1 |
20100131765 | Bromley et al. | May 2010 | A1 |
20100149966 | Achlioptas et al. | Jun 2010 | A1 |
20100191783 | Mason et al. | Jul 2010 | A1 |
20100192157 | Jackson et al. | Jul 2010 | A1 |
20100205601 | Abbas et al. | Aug 2010 | A1 |
20100211782 | Auradkar et al. | Aug 2010 | A1 |
20100293270 | Augenstein et al. | Nov 2010 | A1 |
20100299437 | Moore | Nov 2010 | A1 |
20100318609 | Lahiri et al. | Dec 2010 | A1 |
20100325199 | Park et al. | Dec 2010 | A1 |
20100325441 | Laurie et al. | Dec 2010 | A1 |
20100333116 | Prahlad et al. | Dec 2010 | A1 |
20110016214 | Jackson | Jan 2011 | A1 |
20110035754 | Srinivasan | Feb 2011 | A1 |
20110055396 | Dehaan | Mar 2011 | A1 |
20110055398 | Dehaan et al. | Mar 2011 | A1 |
20110055470 | Portolani | Mar 2011 | A1 |
20110072489 | Parann-Nissany | Mar 2011 | A1 |
20110075667 | Li et al. | Mar 2011 | A1 |
20110110382 | Jabr et al. | May 2011 | A1 |
20110116443 | Yu et al. | May 2011 | A1 |
20110126099 | Anderson et al. | May 2011 | A1 |
20110138055 | Daly et al. | Jun 2011 | A1 |
20110145413 | Dawson et al. | Jun 2011 | A1 |
20110145657 | Bishop et al. | Jun 2011 | A1 |
20110173303 | Rider | Jul 2011 | A1 |
20110185063 | Head et al. | Jul 2011 | A1 |
20110185065 | Stanisic et al. | Jul 2011 | A1 |
20110206052 | Tan et al. | Aug 2011 | A1 |
20110213966 | Fu et al. | Sep 2011 | A1 |
20110219434 | Betz et al. | Sep 2011 | A1 |
20110231715 | Kunii et al. | Sep 2011 | A1 |
20110231899 | Pulier et al. | Sep 2011 | A1 |
20110239039 | Dieffenbach et al. | Sep 2011 | A1 |
20110252327 | Awasthi et al. | Oct 2011 | A1 |
20110261811 | Battestilli et al. | Oct 2011 | A1 |
20110261828 | Smith | Oct 2011 | A1 |
20110276675 | Singh et al. | Nov 2011 | A1 |
20110276951 | Jain | Nov 2011 | A1 |
20110283013 | Grosser et al. | Nov 2011 | A1 |
20110295998 | Ferris et al. | Dec 2011 | A1 |
20110305149 | Scott et al. | Dec 2011 | A1 |
20110307531 | Gaponenko et al. | Dec 2011 | A1 |
20110320870 | Kenigsberg et al. | Dec 2011 | A1 |
20120005724 | Lee | Jan 2012 | A1 |
20120036234 | Staats et al. | Feb 2012 | A1 |
20120054367 | Ramakrishnan et al. | Mar 2012 | A1 |
20120072318 | Akiyama et al. | Mar 2012 | A1 |
20120072578 | Alam | Mar 2012 | A1 |
20120072581 | Tung et al. | Mar 2012 | A1 |
20120072985 | Davne et al. | Mar 2012 | A1 |
20120072992 | Arasaratnam et al. | Mar 2012 | A1 |
20120084445 | Brock et al. | Apr 2012 | A1 |
20120084782 | Chou et al. | Apr 2012 | A1 |
20120096134 | Suit | Apr 2012 | A1 |
20120102193 | Rathore et al. | Apr 2012 | A1 |
20120102199 | Hopmann et al. | Apr 2012 | A1 |
20120131174 | Ferris et al. | May 2012 | A1 |
20120137215 | Kawara | May 2012 | A1 |
20120158967 | Sedayao et al. | Jun 2012 | A1 |
20120159097 | Jennas, II et al. | Jun 2012 | A1 |
20120167094 | Suit | Jun 2012 | A1 |
20120173710 | Rodriguez | Jul 2012 | A1 |
20120179909 | Sagi et al. | Jul 2012 | A1 |
20120180044 | Donnellan et al. | Jul 2012 | A1 |
20120182891 | Lee et al. | Jul 2012 | A1 |
20120185913 | Martinez et al. | Jul 2012 | A1 |
20120192016 | Gotesdyner et al. | Jul 2012 | A1 |
20120192075 | Ebtekar et al. | Jul 2012 | A1 |
20120201135 | Ding et al. | Aug 2012 | A1 |
20120214506 | Skaaksrud et al. | Aug 2012 | A1 |
20120222106 | Kuehl | Aug 2012 | A1 |
20120236716 | Anbazhagan et al. | Sep 2012 | A1 |
20120240113 | Hur | Sep 2012 | A1 |
20120265976 | Spiers et al. | Oct 2012 | A1 |
20120272025 | Park et al. | Oct 2012 | A1 |
20120281706 | Agarwal et al. | Nov 2012 | A1 |
20120281708 | Chauhan et al. | Nov 2012 | A1 |
20120290647 | Ellison et al. | Nov 2012 | A1 |
20120297238 | Watson et al. | Nov 2012 | A1 |
20120311106 | Morgan | Dec 2012 | A1 |
20120311568 | Jansen | Dec 2012 | A1 |
20120324092 | Brown et al. | Dec 2012 | A1 |
20120324114 | Dutta et al. | Dec 2012 | A1 |
20130003567 | Gallant et al. | Jan 2013 | A1 |
20130013248 | Brugler et al. | Jan 2013 | A1 |
20130036213 | Hasan et al. | Feb 2013 | A1 |
20130044636 | Koponen et al. | Feb 2013 | A1 |
20130066940 | Shao | Mar 2013 | A1 |
20130080509 | Wang | Mar 2013 | A1 |
20130080624 | Nagai et al. | Mar 2013 | A1 |
20130091557 | Gurrapu | Apr 2013 | A1 |
20130097601 | Podvratnik et al. | Apr 2013 | A1 |
20130104140 | Meng et al. | Apr 2013 | A1 |
20130111540 | Sabin | May 2013 | A1 |
20130117337 | Dunham | May 2013 | A1 |
20130124712 | Parker | May 2013 | A1 |
20130125124 | Kempf et al. | May 2013 | A1 |
20130129149 | Nandakumar | May 2013 | A1 |
20130138816 | Kuo et al. | May 2013 | A1 |
20130144978 | Jain et al. | Jun 2013 | A1 |
20130152076 | Patel | Jun 2013 | A1 |
20130152175 | Hromoko et al. | Jun 2013 | A1 |
20130159097 | Schory et al. | Jun 2013 | A1 |
20130159496 | Hamilton et al. | Jun 2013 | A1 |
20130160008 | Cawlfield et al. | Jun 2013 | A1 |
20130162753 | Hendrickson et al. | Jun 2013 | A1 |
20130169666 | Pacheco et al. | Jul 2013 | A1 |
20130179941 | McGloin et al. | Jul 2013 | A1 |
20130182712 | Aguayo et al. | Jul 2013 | A1 |
20130185433 | Zhu et al. | Jul 2013 | A1 |
20130191106 | Kephart et al. | Jul 2013 | A1 |
20130198374 | Zalmanovitch et al. | Aug 2013 | A1 |
20130201989 | Hu et al. | Aug 2013 | A1 |
20130204849 | Chacko | Aug 2013 | A1 |
20130232491 | Radhakrishnan et al. | Sep 2013 | A1 |
20130246588 | Borowicz et al. | Sep 2013 | A1 |
20130250770 | Zou et al. | Sep 2013 | A1 |
20130254415 | Fullen et al. | Sep 2013 | A1 |
20130262347 | Dodson | Oct 2013 | A1 |
20130283364 | Chang et al. | Oct 2013 | A1 |
20130297769 | Chang et al. | Nov 2013 | A1 |
20130318240 | Hebert et al. | Nov 2013 | A1 |
20130318546 | Kothuri et al. | Nov 2013 | A1 |
20130339949 | Spiers et al. | Dec 2013 | A1 |
20140006481 | Frey et al. | Jan 2014 | A1 |
20140006535 | Reddy | Jan 2014 | A1 |
20140006585 | Dunbar et al. | Jan 2014 | A1 |
20140040473 | Ho et al. | Feb 2014 | A1 |
20140040883 | Tompkins | Feb 2014 | A1 |
20140052877 | Mao | Feb 2014 | A1 |
20140056146 | Hu et al. | Feb 2014 | A1 |
20140059310 | Du et al. | Feb 2014 | A1 |
20140074850 | Noel et al. | Mar 2014 | A1 |
20140075048 | Yuksel et al. | Mar 2014 | A1 |
20140075108 | Dong et al. | Mar 2014 | A1 |
20140075357 | Flores et al. | Mar 2014 | A1 |
20140075501 | Srinivasan et al. | Mar 2014 | A1 |
20140089727 | Cherkasova et al. | Mar 2014 | A1 |
20140098762 | Ghai et al. | Apr 2014 | A1 |
20140108985 | Scott et al. | Apr 2014 | A1 |
20140122560 | Ramey et al. | May 2014 | A1 |
20140136779 | Guha et al. | May 2014 | A1 |
20140140211 | Chandrasekaran et al. | May 2014 | A1 |
20140141720 | Princen et al. | May 2014 | A1 |
20140156557 | Zeng et al. | Jun 2014 | A1 |
20140164486 | Ravichandran et al. | Jun 2014 | A1 |
20140188825 | Muthukkaruppan et al. | Jul 2014 | A1 |
20140189095 | Lindberg et al. | Jul 2014 | A1 |
20140189125 | Amies et al. | Jul 2014 | A1 |
20140215471 | Cherkasova | Jul 2014 | A1 |
20140222953 | Karve et al. | Aug 2014 | A1 |
20140244851 | Lee | Aug 2014 | A1 |
20140245298 | Zhou et al. | Aug 2014 | A1 |
20140281173 | Im et al. | Sep 2014 | A1 |
20140282536 | Dave et al. | Sep 2014 | A1 |
20140282611 | Campbell et al. | Sep 2014 | A1 |
20140282889 | Ishaya et al. | Sep 2014 | A1 |
20140289200 | Kato | Sep 2014 | A1 |
20140295831 | Karra et al. | Oct 2014 | A1 |
20140297569 | Clark et al. | Oct 2014 | A1 |
20140297835 | Buys | Oct 2014 | A1 |
20140310391 | Sorenson, III et al. | Oct 2014 | A1 |
20140310417 | Sorenson, III et al. | Oct 2014 | A1 |
20140310418 | Sorenson, III et al. | Oct 2014 | A1 |
20140314078 | Jilani | Oct 2014 | A1 |
20140317261 | Shatzkamer et al. | Oct 2014 | A1 |
20140321278 | Cafarelli et al. | Oct 2014 | A1 |
20140330976 | van Bemmel | Nov 2014 | A1 |
20140330977 | van Bemmel | Nov 2014 | A1 |
20140334488 | Guichard et al. | Nov 2014 | A1 |
20140362682 | Guichard et al. | Dec 2014 | A1 |
20140365680 | van Bemmel | Dec 2014 | A1 |
20140366155 | Chang et al. | Dec 2014 | A1 |
20140369204 | Anand et al. | Dec 2014 | A1 |
20140372567 | Ganesh et al. | Dec 2014 | A1 |
20140379938 | Bosch et al. | Dec 2014 | A1 |
20150033086 | Sasturkar et al. | Jan 2015 | A1 |
20150043576 | Dixon et al. | Feb 2015 | A1 |
20150052247 | Threefoot et al. | Feb 2015 | A1 |
20150052517 | Raghu et al. | Feb 2015 | A1 |
20150058382 | St. Laurent et al. | Feb 2015 | A1 |
20150058459 | Amendjian et al. | Feb 2015 | A1 |
20150071285 | Kumar et al. | Mar 2015 | A1 |
20150085870 | Narasimha et al. | Mar 2015 | A1 |
20150089082 | Patwardhan et al. | Mar 2015 | A1 |
20150100471 | Curry, Jr. et al. | Apr 2015 | A1 |
20150103827 | Quinn et al. | Apr 2015 | A1 |
20150106802 | Ivanov et al. | Apr 2015 | A1 |
20150106805 | Melander et al. | Apr 2015 | A1 |
20150117199 | Chinnaiah Sankaran et al. | Apr 2015 | A1 |
20150117458 | Gurkan et al. | Apr 2015 | A1 |
20150120914 | Wada et al. | Apr 2015 | A1 |
20150124622 | Kovvali et al. | May 2015 | A1 |
20150138973 | Kumar et al. | May 2015 | A1 |
20150178133 | Phelan et al. | Jun 2015 | A1 |
20150189009 | van Bemmel | Jul 2015 | A1 |
20150215819 | Bosch et al. | Jul 2015 | A1 |
20150227405 | Jan et al. | Aug 2015 | A1 |
20150242204 | Hassine et al. | Aug 2015 | A1 |
20150249709 | Teng et al. | Sep 2015 | A1 |
20150263901 | Kumar et al. | Sep 2015 | A1 |
20150280980 | Bitar | Oct 2015 | A1 |
20150281067 | Wu | Oct 2015 | A1 |
20150281113 | Siciliano et al. | Oct 2015 | A1 |
20150309908 | Pearson et al. | Oct 2015 | A1 |
20150319063 | Zourzouvillys et al. | Nov 2015 | A1 |
20150326524 | Tankala et al. | Nov 2015 | A1 |
20150339210 | Kopp et al. | Nov 2015 | A1 |
20150358850 | La Roche, Jr. et al. | Dec 2015 | A1 |
20150365324 | Kumar et al. | Dec 2015 | A1 |
20150373108 | Fleming et al. | Dec 2015 | A1 |
20160011925 | Kulkarni et al. | Jan 2016 | A1 |
20160013990 | Kulkarni et al. | Jan 2016 | A1 |
20160026684 | Mukherjee et al. | Jan 2016 | A1 |
20160062786 | Meng et al. | Mar 2016 | A1 |
20160094389 | Jain et al. | Mar 2016 | A1 |
20160094398 | Choudhury et al. | Mar 2016 | A1 |
20160094453 | Jain et al. | Mar 2016 | A1 |
20160094454 | Jain et al. | Mar 2016 | A1 |
20160094455 | Jain et al. | Mar 2016 | A1 |
20160094456 | Jain et al. | Mar 2016 | A1 |
20160094480 | Kulkarni et al. | Mar 2016 | A1 |
20160094643 | Jain et al. | Mar 2016 | A1 |
20160099847 | Melander et al. | Apr 2016 | A1 |
20160099853 | Nedeltchev et al. | Apr 2016 | A1 |
20160099864 | Akiya et al. | Apr 2016 | A1 |
20160105393 | Thakkar et al. | Apr 2016 | A1 |
20160127184 | Bursell | May 2016 | A1 |
20160134557 | Steinder et al. | May 2016 | A1 |
20160156708 | Jalan et al. | Jun 2016 | A1 |
20160164780 | Timmons et al. | Jun 2016 | A1 |
20160164914 | Madhav et al. | Jun 2016 | A1 |
20160182378 | Basavaraja et al. | Jun 2016 | A1 |
20160188527 | Cherian et al. | Jun 2016 | A1 |
20160234071 | Nambiar et al. | Aug 2016 | A1 |
20160239399 | Babu et al. | Aug 2016 | A1 |
20160248861 | Lawson et al. | Aug 2016 | A1 |
20160253078 | Ebtekar et al. | Sep 2016 | A1 |
20160254968 | Ebtekar et al. | Sep 2016 | A1 |
20160261564 | Foxhoven et al. | Sep 2016 | A1 |
20160277368 | Narayanaswamy et al. | Sep 2016 | A1 |
20170005948 | Melander et al. | Jan 2017 | A1 |
20170024260 | Chandrasekaran et al. | Jan 2017 | A1 |
20170026294 | Basavaraja et al. | Jan 2017 | A1 |
20170026470 | Bhargava et al. | Jan 2017 | A1 |
20170041342 | Efremov et al. | Feb 2017 | A1 |
20170046146 | Jamjoom | Feb 2017 | A1 |
20170054659 | Ergin et al. | Feb 2017 | A1 |
20170097841 | Chang et al. | Apr 2017 | A1 |
20170099188 | Chang et al. | Apr 2017 | A1 |
20170104755 | Arregoces et al. | Apr 2017 | A1 |
20170147297 | Krishnamurthy et al. | May 2017 | A1 |
20170149878 | Mutnuru | May 2017 | A1 |
20170163531 | Kumar et al. | Jun 2017 | A1 |
20170171158 | Hoy et al. | Jun 2017 | A1 |
20170264663 | Bicket et al. | Sep 2017 | A1 |
20170339070 | Chang et al. | Nov 2017 | A1 |
20180034832 | Ahuja | Feb 2018 | A1 |
20180054477 | Chivukula | Feb 2018 | A1 |
20180103064 | Ahuja | Apr 2018 | A1 |
20180181390 | Lepcha | Jun 2018 | A1 |
20180225111 | Barger | Aug 2018 | A1 |
20180254996 | Kairali | Sep 2018 | A1 |
20180287876 | Strobel | Oct 2018 | A1 |
20180287883 | Joshi | Oct 2018 | A1 |
Number | Date | Country |
---|---|---|
101719930 | Jun 2010 | CN |
101394360 | Jul 2011 | CN |
102164091 | Aug 2011 | CN |
104320342 | Jan 2015 | CN |
105740084 | Jul 2016 | CN |
2228719 | Sep 2010 | EP |
2439637 | Apr 2012 | EP |
2645253 | Nov 2014 | EP |
10-2015-0070676 | May 2015 | KR |
M394537 | Dec 2010 | TW |
WO 2009155574 | Dec 2009 | WO |
WO 2010030915 | Mar 2010 | WO |
WO 2013158707 | Oct 2013 | WO |
Entry |
---|
Bose (Open and Closed Networks—2002). |
Caesar (Performance Analysis—2010). |
Son (An analysis of the optimal number of servers in distributed client-server environments—2002). |
Szpankowski (Stability Criteria for Queueing Networks—1994). |
University of Bristol—Simple queueing models—2012. |
Amedro, Brian, et al., “An Efficient Framework for Running Applications on Clusters, Grids and Cloud,” 2010, 17 pages. |
Author Unknown, “5 Benefits of a Storage Gateway in the Cloud,” Blog, TwinStrata, Inc., Jul. 25, 2012, XP055141645, 4 pages, https://web.archive.org/web/20120725092619/http://blog.twinstrata.com/2012/07/10//5-benefits-of-a-storage-gateway-in-the-cloud. |
Author Unknown, “Joint Cisco and VMWare Solution for Optimizing Virtual Desktop Delivery: Data Center 3.0: Solutions to Accelerate Data Center Virtualization,” Cisco Systems, Inc. and VMware, Inc., Sep. 2008, 10 pages. |
Author Unknown, “A Look at DeltaCloud: The Multi-Cloud API,” Feb. 17, 2012, 4 pages. |
Author Unknown, “About Deltacloud,” Apache Software Foundation, Aug. 18, 2013, 1 page. |
Author Unknown, “Architecture for Managing Clouds, A White Paper from the Open Cloud Standards Incubator,” Version 1.0.0, Document No. DSP-IS0102, Jun. 18, 2010, 57 pages. |
Author Unknown, “Cloud Infrastructure Management Interface—Common Information Model (CIMI-CIM),” Document No. DSP0264, Version 1.0.0, Dec. 14, 2012, 21 pages. |
Author Unknown, “Cloud Infrastructure Management Interface (CIMI) Primer,” Document No. DSP2027, Version 1.0.1, Sep. 12, 2012, 30 pages. |
Author Unknown, “cloudControl Documentation,” Aug. 25, 2013, 14 pages. |
Author Unknown, “Interoperable Clouds, A White Paper from the Open Cloud Standards Incubator,” Version 1.0.0, Document No. DSP-IS0101, Nov. 11, 2009, 21 pages. |
Author Unknown, “Microsoft Cloud Edge Gateway (MCE) Series Appliance,” Iron Networks, Inc., 2014, 4 pages. |
Author Unknown, “Open Data Center Alliance Usage: Virtual Machine (VM) Interoperability in a Hybrid Cloud Environment Rev. 1.2,” Open Data Center Alliance, Inc., 2013, 18 pages. |
Author Unknown, “Real-Time Performance Monitoring on Juniper Networks Devices, Tips and Tools for Assessing and Analyzing Network Efficiency,” Juniper Networks, Inc., May 2010, 35 pages. |
Author Unknown, “Use Cases and Interactions for Managing Clouds, A White Paper from the Open Cloud Standards Incubator,” Version 1.0.0, Document No. DSP-ISO0103, Jun. 16, 2010, 75 ppages. |
Author Unknown, “Apache Ambari Meetup What's New,” Hortonworks Inc., Sep. 2013, 28 pages. |
Author Unknown, “Introduction,” Apache Ambari project, Apache Software Foundation, 2014, 1 page. |
Baker, F., “Requirements for IP Version 4 Routers,” Jun. 1995, 175 pages, Network Working Group, Cisco Systems. |
Beyer, Steffen, “Module “Data::Locations?!”,” YAPC::Europe, London, UK,ICA, Sep. 22-24, 2000, XP002742700, 15 pages. |
Blanchet, M., “A Flexible Method for Managing the Assignment of Bits of an IPv6 Address Block,” Apr. 2003, 8 pages, Network Working Group, Viagnie. |
Borovick, Lucinda, et al., “Architecting the Network for the Cloud,” IDC White Paper, Jan. 2011, 8 pages. |
Bosch, Greg, “Virtualization,” last modified Apr. 2012 by B. Davison, 33 pages. |
Broadcasters Audience Research Board, “What's Next,” http://lwww.barb.co.uk/whats-next, accessed Jul. 22, 2015, 2 pages. |
Cisco Systems, Inc. “Best Practices in Deploying Cisco Nexus 1000V Series Switches on Cisco UCS B and C Series Cisco UCS Manager Servers,” Cisco White Paper, Apr. 2011, 36 pages, http://www.cisco.com/en/US/prod/collateral/switches/ps9441/ps9902/white_paper_c11-558242.pdf. |
Cisco Systems, Inc., “Cisco Unified Network Services: Overcome Obstacles to Cloud-Ready Deployments,” Cisco White Paper, Jan. 2011, 6 pages. |
Cisco Systems, Inc., “Cisco Intercloud Fabric: Hybrid Cloud with Choice, Consistency, Control and Compliance,” Dec. 10, 2014, 22 pages. |
Cisco Technology, Inc., “Cisco Expands Videoscape TV Platform Into the Cloud,” Jan. 6, 2014, Las Vegas, Nevada, Press Release, 3 pages. |
Citrix, “Citrix StoreFront 2.0” White Paper, Proof of Concept Implementation Guide, Citrix Systems, Inc., 2013, 48 pages. |
Citrix, “CloudBridge for Microsoft Azure Deployment Guide,” 30 pages. |
Citrix, “Deployment Practices and Guidelines for NetScaler 10.5 on Amazon Web Services,” White Paper, citrix.com, 2014, 14 pages. |
CSS Corp, “Enterprise Cloud Gateway (ECG)—Policy driven framework for managing multi-cloud environments,” original published on or about Feb. 11, 2012; 1 page; http://www.css-cloud.com/platform/enterprise-cloud-gateway.php. |
Fang K., “LISP MAC-EID-TO-RLOC Mapping (LISP based L2VPN),” Network Working Group, Internet Draft, CISCO Systems, Jan. 2012, 12 pages. |
Ford, Bryan, et al., Peer-to-Peer Communication Across Network Address Translators, In USENIX Annual Technical Conference, 2005, pp. 179-192. |
Gedymin, Adam, “Cloud Computing with an emphasis on Google App Engine,” Sep. 2011, 146 pages. |
Good, Nathan A., “Use Apache Deltacloud to administer multiple instances with a single API,” Dec. 17, 2012, 7 pages. |
Herry, William, “Keep It Simple, Stupid: OpenStack nova-scheduler and its algorithm”, May 12, 2012, IBM, 12 pages. |
Hewlett-Packard Company, “Virtual context management on network devices”, Research Disclosure, vol. 564, No. 60, Apr. 1, 2011, Mason Publications, Hampshire, GB, Apr. 1, 2011, 524. |
Juniper Networks, Inc., “Recreating Real Application Traffic in Junosphere Lab,” Solution Brief, Dec. 2011, 3 pages. |
Kenhui, “Musings On Cloud Computing and IT-as-a-Service: [Updated for Havana] Openstack Computer for VSphere Admins, Part 2: Nova-Scheduler and DRS”, Jun. 26, 2013, Cloud Architect Musings, 12 pages. |
Kolyshkin, Kirill, “Virtualization in Linux,” Sep. 1, 2006, XP055141648, 5 pages, https://web.archive.ora/web/20070120205111/http://download.openvz.ora/doc/openvz-intro.pdf. |
Kumar, S., et al., “Infrastructure Service Forwarding For NSH,”Service Function Chaining Internet Draft, draft-kumar-sfc-nsh-forwarding-00, Dec. 5, 2015, 10 pages. |
Kunz, Thomas, et al., “OmniCloud—The Secure and Flexible Use of Cloud Storage Services,” 2014, 30 pages. |
Lerach, S.R.O., “Golem,” http://www.lerach.cz/en/products/golem, accessed Jul. 22, 2015, 2 pages. |
Linthicum, David, “VM Import could be a game changer for hybrid clouds”, InfoWorld, Dec. 23, 2010, 4 pages. |
Logan, Marcus, “Hybrid Cloud Application Architecture for Elastic Java-Based Web Applications,” F5 Deployment Guide Version 1.1, 2016, 65 pages. |
Lynch, Sean, “Monitoring cache with Claspin” Facebook Engineering, Sep. 19, 2012, 5 pages. |
Meireles, Fernando Miguel Dias, “Integrated Management of Cloud Computing Resources,” 2013-2014, 286 pages. |
Meraki, “meraki releases industry's first cloud-managed routers,” Jan. 13, 2011, 2 pages. |
Mu, Shuai, et al., “uLibCloud: Providing High Available and Uniform Accessing to Multiple Cloud Storages,” 2012 IEEE, 8 pages. |
Naik, Vijay K., et al., “Harmony: A Desktop Grid for Delivering Enterprise Computations,” Grid Computing, 2003, Fourth International Workshop on Proceedings, Nov. 17, 2003, pp. 1-11. |
Nair, Srijith K. et al., “Towards Secure Cloud Bursting, Brokerage and Aggregation,” 2012, 8 pages, www.flexiant.com. |
Nielsen, “SimMetry Audience Measurement—Technology,” http://www.nielsen-admosphere.eu/products-and-services/simmetry-audience-measurement-technology/, accessed Jul. 22, 2015, 6 pages. |
Nielsen, “Television,” http://www.nielsen.com/us/en/solutions/measurement/television.html, accessed Jul. 22, 2015, 4 pages. |
Open Stack, “Filter Scheduler,” updated Dec. 17, 2017, 5 pages, accessed on Dec. 18, 2017, https://docs.openstack.org/nova/latest/user/filter-scheduler.html. |
Quinn, P., et al., “Network Service Header,” Internet Engineering Task Force Draft, Jul. 3, 2014, 27 pages. |
Quinn, P., et al., “Service Function Chaining (SFC) Architecture,” Network Working Group, Internet Draft, draft-quinn-sfc-arch-03.txt, Jan. 22, 2014, 21 pages. |
Rabadan, J., et al., “Operational Aspects of Proxy-ARP/ND in EVPN Networks,” BESS Worksgroup Internet Draft, draft-snr-bess-evpn-proxy-arp-nd-02, Oct. 6, 2015, 22 pages. |
Saidi, Ali, et al., “Performance Validation of Network-Intensive Workloads on a Full-System Simulator,” Interaction between Operating System and Computer Architecture Workshop, (IOSCA 2005), Austin, Texas, Oct. 2005, 10 pages. |
Shunra, “Shunra for HP Software; Enabling Confidence in Application Performance Before Deployment,” 2010, 2 pages. |
Son, Jungmin, “Automatic decision system for efficient resource selection and allocation in inter-clouds,” Jun. 2013, 35 pages. |
Sun, Aobing, et al., “IaaS Public Cloud Computing Platform Scheduling Model and Optimization Analysis,” Int. J. Communications, Network and System Sciences, 2011, 4, 803-811, 9 pages. |
Szymaniak, Michal, et al., “Latency-Driven Replica Placement”, vol. 47 No. 8, IPSJ Journal, Aug. 2006, 12 pages. |
Toews, Everett, “Introduction to Apache jclouds,” Apr. 7, 2014, 23 pages. |
Von Laszewski, Gregor, et al., “Design of a Dynamic Provisioning System for a Federated Cloud and Bare-metal Environment,” 2012, 8 pages. |
Wikipedia, “Filter (software)”, Wikipedia, Feb. 8, 2014, 2 pages, https://en.wikipedia.org/w/index.php?title=Filter_%28software%29&oldid=594544359. |
Wikipedia; “Pipeline (Unix)”, Wikipedia, May 4, 2014, 4 pages, https://en.wikipedia.ora/w/index.php?title=Pipeline2/028Unix%29&oldid=606980114. |
Ye, Xianglong, et al., “A Novel Blocks Placement Strategy for Hadoop,” 2012 IEEE/ACTS 11th International Conference on Computer and Information Science, 2012 IEEE, 5 pages. |
Ji, Z., et al., “A dynamic deployment method of micro service oriented to SLA,” International Journal of Computer Science Issues, vol. 13, Issue 6, Nov. 2016, 7 pages; http://search.proquest.com/openview/440e284da6426120a8daa78e0f4643c8/1?pq-origsite=gscholar&cbl=55228. |
Wang, J., et al. “An Approach To Modeling Saas-Oriented Software Service Processes,” 2012 International Conference on System Science and Engineering, Jun. 30-Jul. 2, 2012, 5 pages. |
Number | Date | Country | |
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20180295030 A1 | Oct 2018 | US |