Event-driven computing

Description

BACKGROUND

Generally described, computing devices utilize a communication network, or a series of communication networks, to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or 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 may allow a single physical computing device to host one or more instances of virtual machines that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical computing device can create, maintain, delete, or otherwise manage virtual machines in a dynamic manner. In turn, users can request computer resources from a data center, including single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources.

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

BRIEF DESCRIPTION OF THE DRAWINGS

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

FIG. 1 is a block diagram depicting an illustrative environment for processing event messages for user requests to execute program codes in a virtual compute system;

FIG. 2 depicts a general architecture of a computing device providing a frontend of a virtual compute system for processing event messages for user requests to execute program codes;

FIG. 3 is a flow diagram illustrating a request processing routine implemented by a frontend of a virtual compute system, according to an example aspect; and

FIG. 4 is a flow diagram illustrating a request routing routine implemented by a frontend of a virtual compute system, according to an example aspect.

DETAILED DESCRIPTION

Companies and organizations no longer need to acquire and manage their own data centers in order to perform computing operations (e.g., execute code, including threads, programs, functions, software, routines, subroutines, processes, etc.). With the advent of cloud computing, storage space and compute power traditionally provided by hardware computing devices can now be obtained and configured in minutes over the Internet. Thus, developers can quickly purchase a desired amount of computing resources without having to worry about acquiring physical machines. Such computing resources are typically purchased in the form of virtual computing resources, or virtual machine instances. These instances of virtual machines are software implementations of physical machines (e.g., computers), which are hosted on physical computing devices, and may contain operating systems and applications that are traditionally provided on physical machines. These virtual machine instances are configured with a set of computing resources (e.g., memory, CPU, disk, network, etc.) that applications running on the virtual machine instances may request and can be utilized in the same manner as physical computers.

However, even when virtual computing resources are purchased (e.g., in the form of virtual machine instances), developers still have to decide how many and what type of virtual machine instances to purchase, and how long to keep them. For example, the costs of using the virtual machine instances may vary depending on the type and the number of hours they are rented. In addition, the minimum time a virtual machine may be rented is typically on the order of hours. Further, developers have to specify the hardware and software resources (e.g., type of operating systems and language runtimes, etc.) to install on the virtual machines. Other concerns that they might have include over-utilization (e.g., acquiring too little computing resources and suffering performance issues), under-utilization (e.g., acquiring more computing resources than necessary to run the codes, and thus overpaying), prediction of change in traffic (e.g., so that they know when to scale up or down), and instance and language runtime startup delay, which can take 3-10 minutes, or longer, even though users may desire computing capacity on the order of seconds or even milliseconds.

There may be services that provide such users with low latency compute capacity that can be utilized to execute their program codes by maintaining a pool of pre-initialized virtual machine instances that are ready for use as soon as a user request is received, delay (sometimes referred to as latency) associated with executing the user code (e.g., instance and language runtime startup time) can be significantly reduced.

In some implementations, code execution on such a virtual compute system can be triggered by one or more events that occur. For example, a trigger (which can be a piece of code that is configured to automatically respond to certain events occurring on the virtual compute system or at an external or auxiliary service) can be created, and when such events occur, the trigger may automatically cause an action to be performed (e.g., by generating a code execution request and sending it to the frontend of the virtual compute system). The systems or services where such a trigger resides may be referred to as an “event source.” Services that may operate as event sources include, but are not limited to, remote storage systems; database systems; message queue systems; data stream services; web services; auditing services; health monitoring services (e.g., for monitoring health status of a virtual compute system); logging services; billing services; resource management systems and services, or any other services or user-defined applications. When an event specified by the trigger occurs at such an event source (e.g., upload of a new file, modification of existing data in a table, new data stream record is received, etc.), a corresponding program code may be caused to execute on the virtual compute system. The program code may perform any actions specified by the user who generated the trigger or by the virtual compute system, such as sending a notification or initiating a workflow. For example, a trigger can be configured to generate an event every time a given database is modified. In such an example, the triggered events can each cause the database modification to be recorded in permanent storage to create an audit trail of the activity in the database (e.g., by causing a program code configured to perform such storing operation to be executed). The events may further cause a program code that sends a notification to the administrator of the database to be executed on the virtual compute system.

However, the actions performed by the triggered code execution (e.g., logging data, sending notifications, etc.) are static in nature and often require a human operator to analyze the data and take further steps to improve, for example, the code execution performance. Thus, an improved method of processing events and code execution requests in a more dynamic and automatic fashion while maintaining low latency and high scalability is desired.

Generally described, aspects of the present disclosure relate to the management of virtual machine instances and containers created therein. Specifically, systems and methods are disclosed which facilitate management of virtual machine instances in a virtual compute system. The virtual compute system maintains a pool of virtual machine instances that have one or more software components (e.g., operating systems, language runtimes, libraries, etc.) loaded thereon. Maintaining the pool of virtual machine instances may involve creating a new instance, acquiring a new instance from an external instance provisioning service, destroying an instance, assigning/reassigning an instance to a user, modifying an instance (e.g., containers or resources therein), etc. The virtual machine instances in the pool can be designated to service user requests to execute program codes. In the present disclosure, the phrases “program code,” “user code,” and “cloud function” may sometimes be interchangeably used. The program codes can be executed in isolated containers that are created on the virtual machine instances. Since the virtual machine instances in the pool have already been booted and loaded with particular operating systems and language runtimes by the time the requests are received, the delay associated with finding compute capacity that can handle the requests (e.g., by executing the user code in one or more containers created on the virtual machine instances) is significantly reduced.

In certain embodiments, a message queue, a message bus, or any other message intermediary service is provided to facilitate transportation or communication of event messages generated in a first programmatic environment (e.g., at an auxiliary service) to the programmatic environment provided by the virtual compute system described herein. To further facilitate propagation and transportation of a triggered event from the first programmatic environment to the virtual compute system, event messages may be generated to include information descriptive of the triggered event, a user associated with a request to execute user code in response to the triggered event, and programmatic information to enable the virtual compute system to convert the event message into a user request for further processing by the virtual compute system. The event message and/or programmatic information contained therein may be structured according to a schema, a code model, or an application programming interface (“API”) to facilitate both creation/generation of the event message at the auxiliary service and conversion/processing of the event message at the virtual compute system. In some instances, the phrases “events,” “event messages,” and “code execution requests” may be used interchangeably in the present disclosure.

In another aspect, a virtual compute system may maintain a pool of virtual machine instances on one or more physical computing devices, where each virtual machine instance has one or more software components loaded thereon. When the virtual compute system receives a request to execute the program code of a user, which specifies one or more computing constraints for executing the program code of the user, the virtual compute system may select a virtual machine instance for executing the program code of the user based on the one or more computing constraints specified by the request and cause the program code of the user to be executed on the selected virtual machine instance.

One benefit provided by the systems and methods described herein is the ability to dynamically improve, update, or modify various aspects of the code execution environment with or without interaction with the users. For example, in response to detecting a negative trend, the virtual compute system may automatically execute certain control functions to remedy or reverse the negative trend. Such control functions may modify user functions or modify one or more parameters used for executing such user functions. For example, the virtual compute system may monitor and log information related to the amount of resources allocated for executing user code. By doing so, the virtual compute system may be able to identify opportunities for improving the performance of the user code execution by adjusting the amount of allocated resources. Error rates may be reduced by increasing the amount of allocated resources in the event of over-utilization, and costs associated with executing the user code may be reduced by decreasing the amount of allocated resources in the event of under-utilization. Thus, rather than writing code execution errors in an error log or sending a notification to the user when the user's functions are failing to execute properly, the systems and methods described herein provide a dynamic and automated solution to address such errors and failures.

Specific embodiments and example applications of the present disclosure will now be described with reference to the drawings. These embodiments and example applications are intended to illustrate, and not limit, the present disclosure.

Illustrative Environment Including Virtual Compute System

With reference to FIG. 1, a block diagram illustrating an embodiment of a virtual environment 100 will be described. The example shown in FIG. 1 includes a virtual environment 100 in which users (e.g., developers, etc.) of user computing devices 102 may run various program codes using the virtual computing resources provided by a virtual compute system 110.

By way of illustration, various example user computing devices 102 are shown in communication with the virtual compute system 110, including a desktop computer, laptop, and a mobile phone. In general, the user computing devices 102 can be any computing device such as a desktop, laptop, mobile phone (or smartphone), tablet, kiosk, wireless device, and other electronic devices. In addition, the user computing devices 102 may include web services running on the same or different data centers, where, for example, different web services may programmatically communicate with each other to perform one or more techniques described herein. Further, the user computing devices 102 may include Internet of Things (IoT) devices such as Internet appliances and connected devices. The virtual compute system 110 may provide the user computing devices 102 with one or more user interfaces, command-line interfaces (CLI), application programming interfaces (API), and/or other programmatic interfaces for generating and uploading user codes, invoking the user codes (e.g., submitting a request to execute the user codes on the virtual compute system 110), scheduling event-based jobs or timed jobs, tracking the user codes, and/or viewing other logging or monitoring information related to their requests and/or user codes. Although one or more embodiments may be described herein as using a user interface, it should be appreciated that such embodiments may, additionally or alternatively, use any CLIs, APIs, or other programmatic interfaces.

The user computing devices 102 access the virtual compute system 110 over a network 104. The network 104 may be any wired network, wireless network, or combination thereof. In addition, 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. For 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 virtual compute system 110 is depicted in FIG. 1 as operating in a distributed computing environment including several computer systems that are interconnected using one or more computer networks. The virtual compute system 110 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 virtual compute system 110 in FIG. 1 should be taken as illustrative and not limiting to the present disclosure. For example, the virtual compute system 110 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 virtual compute system 110 may be implemented in hardware and/or software 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 the environment illustrated FIG. 1, the virtual environment 100 includes a virtual compute system 110, which includes a frontend 120 (including an event/request processing unit 186), a warming pool manager 130, and a worker manager 140. In the depicted example, virtual machine instances (“instances”) 152, 154 are shown in a warming pool 130A managed by the warming pool manager 130, and instances 156, 158 are shown in an active pool 140A managed by the worker manager 140. The illustration of the various components within the virtual compute system 110 is logical in nature and one or more of the components can be implemented by a single computing device or multiple computing devices. For example, the instances 152, 154, 156, 158 can be implemented on one or more physical computing devices in different various geographic regions. Similarly, each of the frontend 120, the warming pool manager 130, and the worker manager 140 can be implemented across multiple physical computing devices. Alternatively, one or more of the frontend 120, the warming pool manager 130, and the worker manager 140 can be implemented on a single physical computing device. In some embodiments, the virtual compute system 110 may comprise multiple frontends, multiple warming pool managers, and/or multiple worker managers. Although four virtual machine instances are shown in the example of FIG. 1, the embodiments described herein are not limited as such, and one skilled in the art will appreciate that the virtual compute system 110 may comprise any number of virtual machine instances implemented using any number of physical computing devices. Similarly, although a single warming pool and a single active pool are shown in the example of FIG. 1, the embodiments described herein are not limited as such, and one skilled in the art will appreciate that the virtual compute system 110 may comprise any number of warming pools and active pools.

In the example of FIG. 1, the virtual compute system 110 is illustrated as being connected to the network 104. In some embodiments, any of the components within the virtual compute system 110 can communicate with other components (e.g., the user computing devices 102 and auxiliary services 106, which may include monitoring/logging/billing services 107, a storage service 108, an instance provisioning service 109, a message queue service 105, and/or other services that may communicate with the virtual compute system 110) of the virtual environment 100 via the network 104. In other embodiments, not all components of the virtual compute system 110 are capable of communicating with other components of the virtual environment 100. In one example, only the frontend 120 may be connected to the network 104, and other components of the virtual compute system 110 may communicate with other components of the virtual environment 100A via the frontend 120. In some embodiments, any of the auxiliary services 106 may be configured to operate as an event triggering service in order to listen for events specified by users of the auxiliary service and trigger generation of event messages for processing by the virtual compute system 110, as described in more detail herein. Thus, for example, the storage service 108 may be configured to operate as an event triggering service in order to provide the capability of executing user code on the virtual compute system 110 in response to events as they occur on the storage service 108.

In one embodiment, the auxiliary services 106 may be configured to associate an event or event type with a particular program code to be executed on the virtual compute system 110 (that is, the auxiliary services 106 may store or have access to data which associates the event with the particular program code). In another embodiment, the auxiliary services 106 may not necessarily associate an event or event type with a particular program code to be executed on the virtual compute system 110, but rather the auxiliary services 106 may generate event messages which the virtual compute system 110 is configured to interpret as being associated with the program code to be executed on the virtual compute system 110 (that is, the virtual compute system 110 may store or have access to data which associates the event with the particular program code). In another embodiment, an intermediary system or service may be configured to handle interpretation and routing of event messages to execute the program code, such that neither the auxiliary services 106 nor the virtual compute system 110 may store or have access to the event-to-program code association data. For example, the auxiliary services 106 may generate an event message that is agnostic to any particular program code to be executed; and the event message may be routed to the virtual compute system 110 (or an intermediary system) which evaluates the event message and associated metadata to determine which program code to execute in response, and initiate a corresponding request to execute the program code.

As mentioned above, any of the auxiliary services 106 may be configured to operate as an event triggering service. These include but are not limited to: remote storage systems; database systems; message queue systems (e.g., a message queue service provided by the virtual compute system 110, a message queue system owned and/or operated by a user or client separate from the virtual compute system 110, and so on); web services; auditing services; health monitoring services (e.g., for monitoring health status of a virtual compute system); logging services; billing services; resource management systems and services (e.g., for managing lifecycles and/or ownership of virtual computing environments and the like); and so on.

Users may use the virtual compute system 110 to execute user code thereon. 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. Alternatively, the user may send a code execution request the virtual compute system 110. The virtual compute 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 virtual compute system 110 may automatically scale up and down based on the volume, thereby relieving the user from the burden of having to worry about over-utilization (e.g., acquiring too little computing resources and suffering performance issues) or under-utilization (e.g., acquiring more computing resources than necessary to run the codes, and thus overpaying).

Frontend

The frontend 120 receives and processes all the requests (sometimes in the form of event messages) to execute user code on the virtual compute system 110. In one embodiment, the frontend 120 serves as a front door to all the other services provided by the virtual compute system 110. The frontend 120 processes the requests and makes sure that the requests are properly authorized. For example, the frontend 120 may determine whether the user associated with the request is authorized to access the user code specified in the request. In some embodiments, the frontend 120 may serve as a front door to fully automated services including the auxiliary services 106 as well as the virtual compute system 110 (e.g., code execution service). In such embodiments, how the frontend 120 processes incoming requests may differ based on the particular service(s) with which the frontend 120 is interacting. For example, the frontend 120 may treat code execution requests originating from the storage service 108 differently from the way the frontend 120 would treat code execution requests originating from the monitoring service 107.

The user 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 “code,” “user code,” and “program code,” may be used interchangeably. Such user code may be executed to achieve a specific task, for example, in connection with a particular web application or mobile application developed by the user. For example, the user codes may be written in JavaScript (node.js), Java, Python, and/or Ruby. The user code may be associated with a set of configurations. Such configurations may include information regarding how the user code will receive an event that may cause the user code to execute (e.g., event handler).

A request to execute user code may include the user code (or the location thereof) and one or more arguments to be used for executing the user code. For example, the user may provide the user code along with the request to execute the user code. In another example, the request may identify a previously uploaded program code (e.g., using the API for uploading the code) by its name or its unique ID. In yet another example, the code may be included in the request as well as uploaded in a separate location (e.g., the storage service 108 or a storage system internal to the virtual compute system 110) prior to the request is received by the virtual compute system 110. The virtual compute system 110 may vary its code execution strategy based on where the code is available at the time the request is processed.

The frontend 120 may receive the request to execute such user codes in response to 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 the user code. As discussed above, any other protocols, including, for example, HTTP, MQTT, and CoAP, may be used to transfer the message containing the code execution request to the frontend 120. The frontend 120 may also receive the request to execute such user codes when an event is detected, such as an event that the user has registered to trigger automatic request generation. For example, the user may configure an auxiliary service 106 to operate as an event triggering service by registering the user code with the auxiliary service 106 and specifying that whenever a particular event occurs (e.g., a new file is uploaded), the request to execute the user code is sent to the frontend 120. Alternatively, the user may have registered a timed job (e.g., execute the user code every 24 hours). In such an example, when the scheduled time arrives for the timed job, the request to execute the user code may be sent to the frontend 120. A timed or scheduled job may be implemented using the techniques of this disclosure to, for example, model the job as an event generated by a timer service. For example, the timer service may generate an event message indicating that it is now time to run a user code, and the virtual compute system 110 may implement a process to run code at a certain time by utilizing the timer service to remind the virtual compute system 110 to run the user code. In yet another example, the frontend 120 may include or have access to a queue of incoming code execution requests, and when the user's batch job is removed from the virtual compute system's work queue, the frontend 120 may process the user request. In yet another example, the request may originate from another component within the virtual compute system 110 or other servers or services not illustrated in FIG. 1.

In yet another example, the request may originate from another component within the virtual compute system 110 or other servers or services not illustrated in FIG. 1. In some embodiments, a request to execute/activate user codes may be generated in response to an event associated with the user computing device 102 or an auxiliary service 106. For example, in response to an end user uploading a new image from a user computing device to an auxiliary service (such as storage service 108) configured to operate as an event triggering service, the event triggering service can trigger a request to execute/activate a code to generate a thumbnail of the image. The code may be hosted in the active pool 120 or downloaded from a storage service storage service 108 to the virtual compute system 110. In some embodiments, the virtual compute system 110 (e.g., via the event/request processing unit 186), may generate an event message in response to a particular event occurring on the virtual compute system 110. For example, when the virtual compute system 110 (or a component thereof) detects that the available capacity in the active pool 140A has fallen below 10%, an event may be generated to send a notification to an administrator of the virtual compute system 110, send a message to the logging service 107 for logging the information, or send a request to the frontend 120 to initiate an active pool capacity adjustment process.

Message Queue Service

In any of the examples described above and throughout this disclosure, an event message representative of a request to execute the user code may be initially received by a message queue service 105 and provided to or placed in a message queue. In some embodiments, the message queue service 105 maintains a separate storage area (e.g., a dead letter queue) for storing event messages that were not successfully processed by the frontend 120. The message queue service 105 may be implemented as a component of the auxiliary services 106 or as a different component. In certain embodiments, the frontend 120 may periodically poll the message queue service 105 to identify and retrieve event messages for processing or re-processing. Event messages may be placed in the message queue for example by the message queue service 105, such as in response to when an event is detected for which the user has registered to trigger automatic generation of a request to execute user code. In some instances it may be desirable or more practical to detect such events, trigger generation of an event message, and provide the event message to the message queue service 105. For example, depending on the embodiment, the message queue service 105 may be configured to allow ordering of event messages so that the event messages are processed in a specific order and/or allow priority handling of event messages so that certain event messages may receive a higher priority than others. In another example, the message queue service 105 may be specifically or specially configured to facilitate transportation of certain types of programmatic events, such as database operations, certain types of data suitable for batch processing, and so on. In one embodiment the message queue service 105 may be configured to provide streaming, and/or ordered transport of messages (e.g., as a sharded set of data). The frontend 120 may then poll the message queue service 105 and retrieve event messages for further processing by the virtual compute system 110. Although the message queue service 105 is illustrated in FIG. 1 as a component external to the virtual compute system 110, in some cases, the message queue service 105 may be implemented as a component of the virtual compute system 110 (e.g., as part of the frontend 120 or another component that communicates with the frontend 120).

In another embodiment, instead of or in combination with using the message queue service 105, the frontend 120 may query the auxiliary services 106 directly to request and receive event messages for further processing, such as via invocation of an API provided by the auxiliary services 106. In another embodiment, the auxiliary services 106 may interface directly with the frontend 120 via one or more APIs and function calls. For example, when an event is detected and an event message is generated, the auxiliary services 106 may invoke an API provided by the frontend 120 to provide the event message directly to the frontend 120, without necessarily providing the event message to the message queue service 105.

Types of Events Processed and Generated by the Frontend

Events generated on the auxiliary services 106 or the virtual compute system 110 may provide a variety of information depending on their type. In some embodiments, such events may be categorized into 5 different types: (i) events about occurrences on the virtual compute system 110 or the auxiliary services 106, (ii) events about the event stream processed by the frontend 120, (iii) events about the events (e.g., aggregates or summaries of the events), (iv) meta-events indicating the various metric levels, and (v) events about user code (e.g., program codes or functions executed on the virtual compute system 110).

For example, the events under category (i) may include an indication that a customer has uploaded a file onto the storage service 108, an indication that the virtual machine instance 156 associated with a particular account owner has been terminated, or an indication that a database associated with the account owner has been modified.

Events under category (ii) may include an indication that the frontend 120 has not received any event for the past 5 minutes, or an indication that the event stream (e.g., a stream of code execution requests) is still working properly. For example, such an indication may be provided to the account owner associated with the event stream, so that the account owner can assess whether the period of inactivity is unusual and take any necessary action.

Events under category (iii) may include an aggregate summary of the events processed by the frontend 120 such as an indication that the frontend 120 should have processed 5,000 events in the last half hour. Such an aggregate summary may further specify the expected number of events to be received from each event source (e.g., an indication that the frontend 120 should have processed 500 events from event source A, 3,000 events from event source B, and 1,500 events from event source C). Such indications may be provided to the users of the virtual compute system 110 along with the actual number of events processed by the frontend 120 so that the users can sanity check their implementations or detect unusual behaviors/bugs by comparing the actual and expected numbers of events.

Events under category (iv) may include various metrics related to performance and code execution on the virtual compute system 110. Such events may include an indication that a user is still running 50 virtual machine instances on the virtual compute system 110, or an indication that the user has 80 TB of data stored in the storage service 108. For example, such an indication may be used to trigger the execution of a program code configured to send an email to an account owner (or the developer who actually wrote the code) informing the account owner of the higher-than-average resource usage. In some embodiments, such a level indication may be used to trigger the execution of a program code configured to adjust the resource levels to accommodate the increase or decrease in usage. For example, the frontend 120, upon detecting such an event, may be configured to execute a control function to increase the resource levels associated with the program code of the account owner, to throttle executions of the program code to operate under the specified resource levels associated with the program code, or to send a message to the account owner requesting the account owner's input on how to proceed in view of the higher-than-average usage or the account owner's approval for increasing the amount of resources allocated for executing the program code.

Events under category (v) may include specific information about code execution on the virtual compute system 110. For example, such information may include an indication that, for the last thousand times that an event was received from event source A, the program code failed to execute properly or timed out. Such an indication may be used to allow users to disable the program code or to modify the program code to better handle the events received from the particular event source identified as being problematic. For example, if the event indicates that the program code has been timing out on each invocation of the program code, the frontend 120, upon detecting such an event, may be configured to execute a control function to increase the duration specified for the user function, or send a message to the account owner requesting the account owner's input on how to proceed in view of the detected trend (e.g., repeatedly timing out) in the execution of the program code. In another example, when the frontend 120 determines that a particular user function has a 1 GB of memory allocated but in the 500 most recent executions of the program code, no more than 128 MB was used for each execution, the frontend 120 may execute a control function to adjust the amount of memory allocated to the particular program code, and send a message to the account owner, indicating that the amount of memory allocated to the particular program code has been adjusted in view of the trend in the execution of the particular program code. In yet another example, the frontend 120 may detect (e.g., based on an error that occurred while attempting to access certain resources of the account owner) that the permission settings for a particular user function are not properly configured, and cause a message to be sent to the account owner (or automatically adjust the permission settings for the particular user function such that the permission settings are properly configured).

Automatic Action in Response to Events

As described above, the virtual compute system 110 may perform a variety of actions in response to the events detected by and/or received from the auxiliary services 106 or one or more components of the virtual compute system 110. For example, the frontend 120 may detect that the account owner has used 75% of the allotted resources (e.g., number of requests, code execution duration, memory, etc.) and cause a message to be sent to the account owner, letting the account owner know that 75% of the allotted resources has been used. In another example, the frontend 120, upon detecting such an event, may cause a control function to be executed to adjust the rate at which the account owner uses the allotted resources. For example, the frontend 120 may cause a portion of the code execution requests received for the account owner to be purposefully throttled, rejected, delayed, directed to a separate storage area for later execution, etc. In another example, the frontend 120 may adjust the amount of resources allocated to each code execution request so that each execution utilizes a reduced amount of resources. If the account owner has specified that 120 MB of memory is to be allocated for each execution of the user code, but the frontend 120 determines that each execution of the user code has been using only 50 MB of memory, once the resource usage reaches a threshold level (e.g., 75%), the frontend 120 may automatically adjust the amount of memory allocated for each execution from 120 MB to 50 MB. For example, the control function may be configured to receive an event message generated in response to the account owner's resource usage reaching the threshold level, and adjust the amount of memory (e.g., or any other resource) allocated for the user code associated with the account owner. Although memory is used here as an example resource that may be monitored and adjusted (automatically or with account owner's approval), the monitored metrics may also include other resource levels (e.g., number of requests, duration, CPU, memory, storage, network, or other resources), the number of concurrent executions, the number of virtual machine instances/containers, the error rates, or any other metrics.

In some embodiments, the frontend 120 may monitor multiple conditions in parallel, and generate an event as soon as any one of those multiple conditions is satisfied. For example, the account owner may wish to stop running any user functions before any overage fees are charged to his or her account. In such an example, the frontend 120 may monitor the resources utilized by the account owner's user functions (e.g., memory, CPU, storage, network, etc.) and any other relevant criteria (e.g., number of requests), and as soon as any of the resources or criteria reaches a threshold value, the frontend 120 may automatically cause a control function to be executed to prevent the account owner from incurring any additional expenses. In another example, the account owner may wish to automatically authorize the virtual compute system 110 to charge the account owner additional fees so that the account owner's user functions can continue to be executed on the virtual compute system 110 without any interruption. In yet another example, the account owner may specify a custom function to be executed on the virtual compute system 110 as soon as one of several conditions is satisfied.

In some embodiments, the virtual compute system 110 may generate an event indicating that a software upgrade, a library update, or a system change has occurred or is expected to occur on the virtual compute system 110. Such an event may be provided to the various user functions on the virtual compute system 110 and/or to the auxiliary services 106, and in response to receiving such an event, a user function may perform an automated test run to determine whether the user function is compatible with the change. Based on the result of the automated test run, the user function may update certain variables, or make other changes to itself or other related user functions. In other embodiments, in response to receiving such an event, a user function may cause the received information to be recorded (e.g., in the storage service 108).

In some embodiments, the frontend 120 may cause one or more control functions to be executed based on a control policy specified by the account owner. If the account owner is cost-sensitive, the account owner may specify a control policy that would minimize the costs associated with using the services provided by the virtual compute system 110. On the other hand, if the account owner values performance over cost, the account owner may specify a control policy that would optimize the code execution performance. In some embodiments, each control policy may be associated with one or more control functions, and the frontend 120 may generate and forward one or more events to such control functions based on the control policy specified by the account owner.

Third Party User Case

In some embodiments, the control function executed by the frontend 120 may depend on the nature of the end user (e.g., customers of the account owner). For example, the account owner may deal with free users and paying users, and both classes of users may utilize the services provided by the account owner. In such an example, the frontend 120 may differentiate how the code execution requests are processed, based on the type of user initiating the code execution request. For example, if the resource usage associated with the account owner is reaching the monthly limit, the frontend 120 may throttle the code execution requests originating from the free users but not the paying users. In such an example, some or all of the code execution requests initiated by the free users may be routed to a separate storage area (e.g., dead letter queue) to be re-processed at a later time. Although the distinction is made here between free users and paying users, the difference in how the code execution requests are processed may be based on any other qualities or characteristics of the third party users. In some embodiments, the virtual compute system 110 may allow the account owner to specify a control policy indicating how the code execution requests associated with his or her account should be treated.

Dynamically Adjusting Metrics Granularity

In some embodiments, the metrics monitored by the virtual compute system 110 may also be dynamically changed. For example, account owner may want to see the log data associated with his or her account only once a day under normal circumstances, but if there is something unusual happening with his or her account, the account owner may want to see more frequent updates (e.g., every minute). In such an example, the frontend 120, upon detecting an occurrence that satisfies certain threshold criteria, may adjust the frequency at which updates are provided to the particular account owner. In some embodiments, the virtual compute system 110 may allow the account owner to specify these threshold criteria. In other embodiments, the virtual compute system 110 may automatically determine these threshold criteria based on past performance associated with the account owner or the user functions associated with the account owner. Similar techniques may be applied to logging. For example, the frontend 120, upon detecting an increased error rate or a spike in the number of events received, may cause a control function to be executed, where the control function is configured to turn up the granularity of the logged metrics (e.g., frequency, breadth, level of detail, etc.). If only errors are logged under normal circumstances, both errors and warnings may be logged when a threshold level of irregularities has been detected.

Dead Letter Queue

In some embodiments, the frontend 120 may receive an event message or a request to execute a program code, and if the frontend 120 is not able to successfully process the event message or the request (e.g., if the program code fails to execute or if there is something wrong with the request), the frontend 120 may send the request to a separate storage area called dead letter queue (DLQ). The requests placed in the DLQ may be re-processed at a later time. The items placed in the DLQ may be inspected by a human operator and/or recorded in a logging system (e.g., logging system 107). In some embodiments, the virtual compute system 110 may send an item placed in the DLQ to a control function configured to handle such requests. In some embodiments, the items placed in the DLQ are processed in the order that the items were placed in the DLQ. In other embodiments, the items placed in the DLQ are processed based on a priority value associated with the respective items. The priority values may be assigned to the items when the items are placed in the DLQ. The priority values assigned to the items in the DLQ may be modified over time (e.g., by the frontend 120 or another component managing the DLQ). In one embodiment, the DLQ is internally managed by the virtual compute system 110. In another embodiment, the DLQ is managed by the message queue service 105. In yet another embodiment, the DLQ is managed by individual account owners. In such an embodiment, the account owners may review the content stored in the DLQ and may decide to send some of the requests stored in the DLQ back to the virtual compute system 110 for re-processing.

Actions Performed on Items in DLQ

When an item (e.g., an event message, a code execution request, etc.) is placed in the DLQ, a variety of actions may be performed on the item depending on the implementation. In one example, the frontend 120 may determine which code version is associated with the code execution request that was placed in the DLQ. In such an example, if the frontend 120 determines that the code version is outdated, the frontend 120 may execute a control function configured to perform an automated update (e.g., to the latest stable version) of the code version of the one or more user functions associated with the code execution request.

In some embodiments, when a code execution request is placed in the DLQ, the frontend 120 may subsequently re-process the request or cause another function or another service to be performed. For example, the frontend 120 may provide an indication that the program code has failed to execute properly to a control function configured to generate logs, and the control function may forward the information to the logging service 107 to be recorded in a log associated with the account owner or to the storage service 108 to be stored in permanent storage associated with the account owner. In such an example, the account owner can later examine the item in the log or storage and determine what went wrong with the program code.

In another embodiment, the frontend 120 may perform analytics on the items that are getting rejected and being sent to the DLQ. By doing so, the frontend 120 may detect patterns in the items that are getting rejected and identify why such items are getting rejected. For example, if the frontend 120 determines that 90% of the rejected code execution requests are associated with a version of the code that was recently updated, and that the code execution requests associated with older versions of the code are only getting rejected 5% of the time, the frontend 120 may identify the code version as the reason that the rejected items are getting rejected. In response to identifying the reason, the frontend 120 may execute a control function configured to initiate an automatic rollback of the code version of the program code. In another case, in response to identifying the reason, the frontend 120 may send a notification to the account holder via a messenger service. In another example, the frontend 120 may perform a pattern matching on the data that are getting rejected and placed in the DLQ. The pattern matching may identify similarities that are shared by the items that are getting rejected. For example, the items may be associated with similar user functions, similar file sizes, similar origin addresses, etc. Based on the identified similarities, the frontend 120 may generate a report that is sent to the account owner or stored in the account owner's storage for later review.

In some embodiments, the frontend 120, after identifying one or more potential problems with the items that are being sent to the DLQ, may make any necessary adjustments and try processing the items again. For example, in response to determining that the code version associated with the code execution request caused the code execution request to fail, the frontend 120 may reconfigure the code execution request such that the reconfigured code execution request is associated with an older, stable version of the code, and process the reconfigured code execution through the virtual compute system 110. In another example, the account owner may utilize an image resizing function to generate thumbnails of the image files that uploaded by the account owner's customers. In such an example, if the frontend 120 determines that all of the code execution requests associated with image file sizes that are larger than 1 GB are getting rejected, the frontend 120 may execute a user function (e.g., specified by the account owner) configure to send a message to those customers, indicating that their requests may be getting rejected due to the large file size and that they should try uploading images having a smaller file size. In another example, if the frontend 120 determines that the code execution requests placed in the DLQ failed because the code execution requests are associated with incorrect permission settings (e.g., thereby causing errors when the code tries to access a particular database during execution), the frontend 120 may reconfigure (e.g., by specifying a different set of permission settings) the code execution requests and/or the program code associated with the code execution requests, and process the reconfigured code execution requests through the virtual compute system 110.

Criteria for Sending an Item to DLQ

In some cases, the criteria applied for sending an item to the DLQ may differ based on the type of item and/or the type of error generated by the item. For example, code execution requests associated with program codes owned and managed by the virtual compute system 110 may be placed in the DLQ after a different number of tries than code execution requests associated with program codes developed by the account owners. In another example, for one class of account owners, code execution requests may be sent to the DLQ only if the code fails to execute, and for another class of account owners, code execution requests may be sent to the DLQ if any warning is generated, even if the code itself executes successfully. In some embodiments, the frontend 120 causes code execution requests associated with program codes owned by the virtual compute system 110 to be sent to the DLQ for subsequent analysis by a human operator. In such embodiments, the frontend 120, upon detecting an error with a particular code execution request, may further determine whether the particular code execution request is associated with a program code that belongs to an account owner or to the virtual compute system 110. In response to determining that the particular code execution request belongs to the virtual compute system 110, the frontend 120 may send the code execution request to the DLQ to be analyzed by a human operator, without modifying the code execution request or the program code. On the other hand, in response to determining that the particular code execution requests belongs to an account owner, the frontend 120 may execute a control function configured to modify the code execution request or the program code itself, and cause the code execution request to be processed again through the virtual compute system 110.

In some embodiments, the virtual compute system 110 determines whether to send a request to the DLQ and whether to retrieve a request from the DLQ for re-processing (or whether to perform any other actions such as logging, discarding, etc.). In other embodiments, the virtual compute system 110 determines whether to send a request to the DLQ, and another system (e.g., the message queue service 105 or another user-controlled system) determines whether to send a request stored in the DLQ back to the virtual compute system 110 for re-processing (or whether to perform any other actions such as logging, discarding, etc.).

Other Data Associated with Code Execution Requests

A user request to execute a program code on the virtual compute system 110 may specify one or more third-party libraries (including native libraries) to be used along with the user code. In one embodiment, the user request includes a package file (e.g., a compressed file, a ZIP file, a RAR file, etc.) containing the user code and any libraries (and/or identifications of storage locations thereof). In some embodiments, the user request includes metadata that indicates the program code to be executed, the language in which the program code is written, the user associated with the request, and/or the computing resources (e.g., memory, etc.) to be reserved for executing the program code. For example, the program code may be provided with the request, previously uploaded by the user, provided by the virtual compute system 110 (e.g., standard routines), and/or provided by third parties. In some embodiments, such resource-level constraints (e.g., how much memory is to be allocated for executing a particular user code) are specified for the particular user code, and may not vary over each execution of the user code. In such cases, the virtual compute system 110 may have access to such resource-level constraints before each individual request is received, and the individual requests may not specify such resource-level constraints. In some embodiments, the user request may specify other constraints such as permission data that indicates what kind of permissions that the request has to execute the user code. Such permission data may be used by the virtual compute system 110 to invoke the user code, pull events from one of the event sources (e.g., auxiliary services 106), access private resources (e.g., on a private network), etc. The permission data may allow the user code to access other resources (e.g., read objects stored in the storage service 108, write logs to the logging service 107, modify data stored in a database service, etc.)

In some embodiments, the user request may specify the behavior that should be adopted for handling the user request. In such embodiments, the user request may include an indicator for enabling one or more execution modes in which the user code associated with the user request is to be executed. For example, the request may include a flag or a header for indicating whether the user code should be executed in a debug mode in which the debugging and/or logging output that may be generated in connection with the execution of the user code is provided back to the user (e.g., via a console user interface). In such an example, the virtual compute system 110 may inspect the request and look for the flag or the header, and if it is present, the virtual compute system 110 may modify the behavior (e.g., logging facilities) of the container in which the user code is executed, and cause the output data to be provided back to the user. In some embodiments, the behavior/mode indicators are added to the request by the user interface provided to the user by the virtual compute system 110. Other features such as source code profiling, remote debugging, etc. may also be enabled or disabled based on the indication provided in the request.

In some embodiments, the virtual compute system 110 may include multiple frontends 120. In such embodiments, a load balancer may be provided to distribute the incoming requests and/or event messages to the multiple frontends 120, for example, in a round-robin fashion. An example architecture of the frontend 120 is described in greater detail below with reference to FIG. 2.

Warming Pool Manager

The warming pool manager 130 ensures that virtual machine instances are ready to be used by the worker manager 140 when the virtual compute system 110 receives a request to execute user code on the virtual compute system 110. In the example illustrated in FIG. 1, the warming pool manager 130 manages the warming pool 130A, which is a group (sometimes referred to as a pool) of pre-initialized and pre-configured virtual machine instances that may be used to service incoming user code execution requests. In some embodiments, the warming pool manager 130 causes virtual machine instances to be booted up on one or more physical computing machines within the virtual compute system 110 and added to the warming pool 130A prior to receiving a code execution request that will be executed on the virtual machine instance. In other embodiments, the warming pool manager 130 communicates with an auxiliary virtual machine instance service (e.g., an instance provisioning service 109) to create and add new instances to the warming pool 130A. For example, the warming pool manager 130 may cause additional instances to be added to the warming pool 130A based on the available capacity in the warming pool 130A to service incoming requests. In some embodiments, the warming pool manager 130 may utilize both physical computing devices within the virtual compute system 110 and one or more virtual machine instance services to acquire and maintain compute capacity that can be used to service code execution requests received by the frontend 120. In some embodiments, the virtual compute system 110 may comprise one or more logical knobs or switches for controlling (e.g., increasing or decreasing) the available capacity in the warming pool 130A. For example, a system administrator may use such a knob or switch to increase the capacity available (e.g., the number of pre-booted instances) in the warming pool 130A during peak hours. In some embodiments, virtual machine instances in the warming pool 130A can be configured based on a predetermined set of configurations independent from a specific user request to execute a user's code. The predetermined set of configurations can correspond to various types of virtual machine instances to execute user codes. The warming pool manager 130 can optimize types and numbers of virtual machine instances in the warming pool 130A based on one or more metrics related to current or previous user code executions.

As shown in FIG. 1, instances may have operating systems (OS) and/or language runtimes loaded thereon. For example, the warming pool 130A managed by the warming pool manager 130 comprises instances 152, 154. The instance 152 includes an OS 152A and a runtime 152B. The instance 154 includes an OS 154A. In some embodiments, the instances in the warming pool 130A may also include containers (which may further contain copies of operating systems, runtimes, user codes, etc.), which are described in greater detail below. Although the instance 152 is shown in FIG. 1 to include a single runtime, in other embodiments, the instances depicted in FIG. 1 may include two or more runtimes, each of which may be used for running a different user code. In some embodiments, the warming pool manager 130 may maintain a list of instances in the warming pool 130A. The list of instances may further specify the configuration (e.g., OS, runtime, container, etc.) of the instances.

In some embodiments, the virtual machine instances in the warming pool 130A may be used to serve any user's request. In one embodiment, all the virtual machine instances in the warming pool 130A are configured in the same or substantially similar manner. In another embodiment, the virtual machine instances in the warming pool 130A may be configured differently to suit the needs of different users. For example, the virtual machine instances may have different operating systems, different language runtimes, and/or different libraries loaded thereon. In yet another embodiment, the virtual machine instances in the warming pool 130A may be configured in the same or substantially similar manner (e.g., with the same OS, language runtimes, and/or libraries), but some of those instances may have different container configurations. For example, two instances may have runtimes for both Python and Ruby, but one instance may have a container configured to run Python code, and the other instance may have a container configured to run Ruby code. In some embodiments, multiple warming pools 130A, each having identically-configured virtual machine instances, are provided.

The warming pool manager 130 may pre-configure the virtual machine instances in the warming pool 130A, such that each virtual machine instance is configured to satisfy at least one of the operating conditions that may be requested or specified by the user request to execute program code on the virtual compute system 110. In one embodiment, the operating conditions may include program languages in which the potential user codes may be written. For example, such languages may include Java, JavaScript, Python, Ruby, and the like. In some embodiments, the set of languages that the user codes may be written in may be limited to a predetermined set (e.g., set of 4 languages, although in some embodiments sets of more or less than four languages are provided) in order to facilitate pre-initialization of the virtual machine instances that can satisfy requests to execute user codes. For example, when the user is configuring a request via a user interface provided by the virtual compute system 110, the user interface may prompt the user to specify one of the predetermined operating conditions for executing the user code. In another example, the service-level agreement (SLA) for utilizing the services provided by the virtual compute system 110 may specify a set of conditions (e.g., programming languages, computing resources, etc.) that user requests should satisfy, and the virtual compute system 110 may assume that the requests satisfy the set of conditions in handling the requests. In another example, operating conditions specified in the request may include: the amount of compute power to be used for processing the request; the type of the request (e.g., HTTP vs. a triggered event); the timeout for the request (e.g., threshold time after which the request may be terminated); security policies (e.g., may control which instances in the warming pool 130A are usable by which user); etc.

Worker Manager

The worker manager 140 manages the instances used for servicing incoming code execution requests. In the example illustrated in FIG. 1, the worker manager 140 manages the active pool 140A, which is a group (sometimes referred to as a pool) of virtual machine instances that are currently assigned to one or more users. Although the virtual machine instances are described here as being assigned to a particular user, in some embodiments, the instances may be assigned to a group of users, such that the instance is tied to the group of users and any member of the group can utilize resources on the instance. For example, the users in the same group may belong to the same security group (e.g., based on their security credentials) such that executing one member's code in a container on a particular instance after another member's code has been executed in another container on the same instance does not pose security risks. Similarly, the worker manager 140 may assign the instances and the containers according to one or more policies that dictate which requests can be executed in which containers and which instances can be assigned to which users. An example policy may specify that instances are assigned to collections of users who share the same account (e.g., account for accessing the services provided by the virtual compute system 110). In some embodiments, the requests associated with the same user group may share the same containers (e.g., if the user codes associated therewith are identical). In some embodiments, a request does not differentiate between the different users of the group and simply indicates the group to which the users associated with the requests belong.

As shown in FIG. 1, instances may have operating systems (OS), language runtimes, and containers. The containers may have individual copies of the OS and the runtimes and user codes loaded thereon. In the example of FIG. 1, the active pool 140A managed by the worker manager 140 includes the instances 156, 158. The instance 156 has an OS 156A, runtimes 156B, 156C, and containers 156D, 156E. The container 156D includes a copy of the OS 156A, a copy of the runtime 156B, and a copy of a code 156D-1. The container 156E includes a copy of the OS 156A, a copy of the runtime 156C, and a copy of a code 156E-1. The instance 158 has an OS 158A, runtimes 158B, 158C, 158E, 158F, a container 158D, and codes 158G, 158H. The container 158D has a copy of the OS 158A, a copy of the runtime 158B, and a copy of a code 158D-1. As illustrated in FIG. 1, instances may have user codes loaded thereon, and containers within those instances may also have user codes loaded therein. In some embodiments, the worker manager 140 may maintain a list of instances in the active pool 140A. The list of instances may further specify the configuration (e.g., OS, runtime, container, etc.) of the instances. In some embodiments, the worker manager 140 may have access to a list of instances in the warming pool 130A (e.g., including the number and type of instances). In other embodiments, the worker manager 140 requests compute capacity from the warming pool manager 130 without having knowledge of the virtual machine instances in the warming pool 130A.

In the example illustrated in FIG. 1, user codes are executed in isolated compute systems referred to as containers (e.g., containers 156D, 156E, 158D). Containers are logical units created within a virtual machine instance using the resources available on that instance. For example, the worker manager 140 may, based on information specified in the request to execute user code, create a new container or locate an existing container in one of the instances in the active pool 140A and assigns the container to the request to handle the execution of the user code associated with the request. In one embodiment, such containers are implemented as Linux containers. The virtual machine instances in the active pool 140A may have one or more containers created thereon and have one or more program codes associated with the user loaded thereon (e.g., either in one of the containers or in a local cache of the instance). Each container may have credential information made available therein, so that user codes executing on the container have access to whatever the corresponding credential information allows them to access.

Once a request has been successfully processed by the frontend 120, the worker manager 140 finds capacity to service the request to execute user code on the virtual compute system 110. For example, if there exists a particular virtual machine instance in the active pool 140A that has a container with the same user code loaded therein (e.g., code 156D-1 shown in the container 156D), the worker manager 140 may assign the container to the request and cause the user code to be executed in the container. Alternatively, if the user code is available in the local cache of one of the virtual machine instances (e.g., codes 158G, 158H, which are stored on the instance 158 but do not belong to any individual containers), the worker manager 140 may create a new container on such an instance, assign the container to the request, and cause the user code to be loaded and executed in the container.

If the worker manager 140 determines that the user code associated with the request is not found on any of the instances (e.g., either in a container or the local cache of an instance) in the active pool 140A, the worker manager 140 may determine whether any of the instances in the active pool 140A is currently assigned to the user associated with the request and has compute capacity to handle the current request. If there is such an instance, the worker manager 140 may create a new container on the instance and assign the container to the request. Alternatively, the worker manager 140 may further configure an existing container on the instance assigned to the user, and assign the container to the request. For example, the worker manager 140 may determine that the existing container may be used to execute the user code if a particular library demanded by the current user request is loaded thereon. In such a case, the worker manager 140 may load the particular library and the user code onto the container and use the container to execute the user code.

If the active pool 140A does not contain any instances currently assigned to the user, the worker manager 140 pulls a new virtual machine instance from the warming pool 130A, assigns the instance to the user associated with the request, creates a new container on the instance, assigns the container to the request, and causes the user code to be downloaded and executed on the container.

The user code may be downloaded from an auxiliary service 106 such as the storage service 108 of FIG. 1. Data 108A illustrated in FIG. 1 may comprise user codes uploaded by one or more users, metadata associated with such user codes, or any other data utilized by the virtual compute system 110 to perform one or more techniques described herein. Although only the storage service 108 is illustrated in the example of FIG. 1, the virtual environment 100 may include other levels of storage systems from which the user code may be downloaded. For example, each instance may have one or more storage systems either physically (e.g., a local storage resident on the physical computing system on which the instance is running) or logically (e.g., a network-attached storage system in network communication with the instance and provided within or outside of the virtual compute system 110) associated with the instance on which the container is created. Alternatively, the code may be downloaded from a web-based data store provided by the storage service 108.

Once the worker manager 140 locates one of the virtual machine instances in the warming pool 130A that can be used to serve the user code execution request, the warming pool manager 130 or the worker manger 140 takes the instance out of the warming pool 130A and assigns it to the user associated with the request. The assigned virtual machine instance is taken out of the warming pool 130A and placed in the active pool 140A. In some embodiments, once the virtual machine instance has been assigned to a particular user, the same virtual machine instance cannot be used to service requests of any other user. This provides security benefits to users by preventing possible co-mingling of user resources. Alternatively, in some embodiments, multiple containers belonging to different users (or assigned to requests associated with different users) may co-exist on a single virtual machine instance. Such an approach may improve utilization of the available compute capacity.

In some embodiments, the virtual compute system 110 may maintain a separate cache in which user codes are stored to serve as an intermediate level of caching system between the local cache of the virtual machine instances and a web-based network storage (e.g., accessible via the network 104). The various scenarios that the worker manager 140 may encounter in servicing the request are described in greater detail below with reference to FIG. 4.

After the user code has been executed, the worker manager 140 may tear down the container used to execute the user code to free up the resources it occupied to be used for other containers in the instance. Alternatively, the worker manager 140 may keep the container running to use it to service additional requests from the same user. For example, if another request associated with the same user code that has already been loaded in the container, the request can be assigned to the same container, thereby eliminating the delay associated with creating a new container and loading the user code in the container. In some embodiments, the worker manager 140 may tear down the instance in which the container used to execute the user code was created. Alternatively, the worker manager 140 may keep the instance running to use it to service additional requests from the same user. The determination of whether to keep the container and/or the instance running after the user code is done executing may be based on a threshold time, the type of the user, average request volume of the user, and/or other operating conditions. For example, after a threshold time has passed (e.g., 5 minutes, 30 minutes, 1 hour, 24 hours, 30 days, etc.) without any activity (e.g., running of the code), the container and/or the virtual machine instance is shutdown (e.g., deleted, terminated, etc.), and resources allocated thereto are released. In some embodiments, the threshold time passed before a container is torn down is shorter than the threshold time passed before an instance is torn down.

Other Services

In some embodiments, the virtual compute system 110 may provide data to one or more of the auxiliary services 106 as it services incoming code execution requests. For example, the virtual compute system 110 may communicate with the monitoring/logging/billing services 107. The monitoring/logging/billing services 107 may include: a monitoring service for managing monitoring information received from the virtual compute system 110, such as statuses of containers and instances on the virtual compute system 110; a logging service for managing logging information received from the virtual compute system 110, such as activities performed by containers and instances on the virtual compute system 110; and a billing service for generating billing information associated with executing user code on the virtual compute system 110 (e.g., based on the monitoring information and/or the logging information managed by the monitoring service and the logging service). In addition to the system-level activities that may be performed by the monitoring/logging/billing services 107 (e.g., on behalf of the virtual compute system 110) as described above, the monitoring/logging/billing services 107 may provide application-level services on behalf of the user code executed on the virtual compute system 110. For example, the monitoring/logging/billing services 107 may monitor and/or log various inputs, outputs, or other data and parameters on behalf of the user code being executed on the virtual compute system 110. Although shown as a single block, the monitoring, logging, and billing services 107 may be provided as separate services.

In some embodiments, the worker manager 140 may perform health checks on the instances and containers managed by the worker manager 140 (e.g., those in the active pool 140A). For example, the health checks performed by the worker manager 140 may include determining whether the instances and the containers managed by the worker manager 140 have any issues of (1) misconfigured networking and/or startup configuration, (2) exhausted memory, (3) corrupted file system, (4) incompatible kernel, and/or any other problems that may impair the performance of the instances and the containers. In one embodiment, the worker manager 140 performs the health checks periodically (e.g., every 5 minutes, every 30 minutes, every hour, every 24 hours, etc.). In some embodiments, the frequency of the health checks may be adjusted automatically based on the result of the health checks. In other embodiments, the frequency of the health checks may be adjusted based on user requests. In some embodiments, the worker manager 140 may perform similar health checks on the instances and/or containers in the warming pool 130A. The instances and/or the containers in the warming pool 130A may be managed either together with those instances and containers in the active pool 140A or separately. In some embodiments, in the case where the health of the instances and/or the containers in the warming pool 130A is managed separately from the active pool 140A, the warming pool manager 130, instead of the worker manager 140, may perform the health checks described above on the instances and/or the containers in the warming pool 130A.

In some embodiments, the virtual compute system 110 is adapted to begin execution of the user code shortly after it is received (e.g., by the frontend 120). A time period can be determined as the difference in time between initiating execution of the user code (e.g., in a container on a virtual machine instance associated with the user) and receiving a request to execute the user code (e.g., received by a frontend). Another time period can be determined as the difference in time between (1) detection of an event on an event-triggering service and (2a) receiving a request to execute the user code (e.g., received by a frontend) and/or (2b) initiating execution of the user code (e.g., in a container on a virtual machine instance associated with the user). Another time period can be determined as the difference in time between (1) retrieving, accessing, or receiving an event message (e.g., directly or indirectly from on an event-triggering service) and (2) initiating processing of a request to execute the user code (e.g., in a container on a virtual machine instance associated with the user). The virtual compute system 110 is adapted to begin execution of the user code within a time period that is less than a predetermined duration. In one embodiment, the predetermined duration is 500 ms. In another embodiment, the predetermined duration is 300 ms. In another embodiment, the predetermined duration is 100 ms. In another embodiment, the predetermined duration is 50 ms. In another embodiment, the predetermined duration is 10 ms. In another embodiment, the predetermined duration may be any value chosen from the range of 10 ms to 500 ms. In some embodiments, the virtual compute system 110 is adapted to begin execution of the user code within a time period that is less than a predetermined duration if one or more conditions are satisfied. For example, the one or more conditions may include any one of: (1) the user code is loaded on a container in the active pool 140A at the time the request is received; (2) the user code is stored in the code cache of an instance in the active pool 140A at the time the request is received; (3) the active pool 140A contains an instance assigned to the user associated with the request at the time the request is received; or (4) the warming pool 130A has capacity to handle the request at the time the request is received.

The worker manager 140 may include an instance allocation unit for finding compute capacity (e.g., containers) to service incoming code execution requests and a user code execution module for facilitating the execution of user codes on those containers.

General Architecture of the Frontend

FIG. 2 depicts a general architecture of a computing system (referenced as frontend 120) that processes (e.g., receives, generates, and/or routes) event messages for user requests to execute program codes in the virtual compute system 110. The general architecture of the frontend 120 depicted in FIG. 2 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 frontend 120 may include many more (or fewer) elements than those shown in FIG. 2. 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. 2 may be used to implement one or more of the other components illustrated in FIG. 1. As illustrated, the frontend 120 includes a processing unit 190, a network interface 192, a computer readable medium drive 194, 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 RAM, 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 an event/request processing unit 186 which may include an event/request handling unit 186A and an event/request routing unit 186B that may be executed by the processing unit 190. In one embodiment, the user interface unit 182, the event/request handling unit 186A, and the event/request routing unit 186B individually or collectively implement various aspects of the present disclosure, e.g., processing a code execution request (or an event message for a request to execute a program code), detecting a trend in the processing of the code execution request, generating a request to execute another program code based on the detected trend, etc. as described herein.

The event/request handling unit 186A may determine that one or more code execution requests have produced unexpected results and select one or more actions to be performed in response to such unexpected results. For example, the event/request handling unit 186A may determine that the amount of available resources associated with a program code has fallen below a threshold level, and determine that a control function that is configured to allocate additional resources for use by the program code should be executed. The event/request routing unit 186B may perform the actions determined by the event/request handling unit 186A. For example, the event/request routing unit 186B may route, for example, code execution requests that were not processed successfully to the dynamic DLQ. In another example, the event/request routing unit 186B may cause a notification to be sent to a user, or a control function configured to either modify the program code or adjust certain parameters or configuration data associated with the program code to be executed.

While the event/request handling unit 186A and the event/request routing unit 186B are shown in FIG. 2 as part of the frontend 120, in other embodiments, all or a portion of the event/request handling unit 186A and the event/request routing unit 186B may be implemented by other components of the virtual compute system 110 and/or another computing device. For example, in certain embodiments of the present disclosure, another computing device in communication with the virtual compute system 110 may include several modules or components that operate similarly to the modules and components illustrated as part of the frontend 120. In some embodiments, the frontend 120 may further include components other than those illustrated in FIG. 2.

Example Routine for Processing Code Execution Requests

Turning now to FIG. 3, a routine 300 implemented by one or more components of the virtual compute system 110 (e.g., the frontend 120) will be described. Although routine 300 is described with regard to implementation by the frontend 120, one skilled in the relevant art will appreciate that alternative components may implement routine 300 or that one or more of the blocks may be implemented by a different component or in a distributed manner.

At block 302 of the illustrative routine 300, the frontend 120 receives a plurality of code execution requests associated with a first program code. The code execution requests may arrive at the frontend 120 in the form of event messages that are associated with (or configured to be received by) the first program code. For example, such event messages may include an identity of the first program code. The event messages and/or the code execution requests may include one or more first input parameters to be used for executing the first program code. In addition, the first program code may be associated with a set of configuration data that is specified for the first program code by, for example, the account owner associated with the first program code. The configuration data may include the resources allocated for executing the first program code. The resources may comprise one or more of CPU, memory, network, input/output (I/O), a maximum number of requests, or a maximum execution duration, or any other computing resources or constraints associated with the first program code.

Next, at block 304, the frontend 120 processes the plurality of code execution requests using the one or more first input parameters and the configuration data. For example, the frontend 120 may cause the first program code to be executed for each of the plurality of code execution requests, by using the first input parameters and the configuration data. In another example, the frontend 120 causes the first program code to be executed for only a subset of the plurality of code execution requests, and determines that other code execution requests have failed due to, for example, an error in the provided input parameters or an insufficient amount of resources allocated to the first program code.

At block 306, the frontend 120 detects a trend in the processing of the plurality of code execution requests. For example, the detected trend may be that code execution requests received at a certain time of day are more likely to fail. In another example, the detected trend may be that 95% of the code execution requests associated with the latest version of the first program code have failed so far. In some embodiments, detecting a trend comprises determining whether the attempted code execution has produced an unexpected result (e.g., failure, error, warning, etc.) or satisfied any triggering conditions (e.g., failed three times in a row, etc.).

At block 308, the frontend 120 selects an action based on the detected trend. For example, the selected action may be one of creating a log detailing the trend or unexpected result, automatically modifying the first program code or parameters associated with the first program code, sending a warning message or notification to the account owner, sending the request to a separate storage area for additional processing, executing a control function to make adjustments (e.g., perform an update, a rollback, etc.) to place the first program code in a better state for execution, etc. For example, if the frontend 120 determined at block 306 that most of the code execution requests associated with the latest version of the first program code have been failing, in response, the frontend 120 may determine that the latest version of the first program code may not be stable and that a rollback may need to be performed.

At block 310, the frontend 120 causes a second program code configured to perform the selected action to be executed. The frontend 120 may determine one or more second input parameters that are used for executing the second program code. As discuss above, the second program code may be configured to perform any one of creating a log detailing the trend or unexpected result, automatically modifying the first program code or parameters associated with the first program code, sending a warning message or notification to the account owner, sending the request to a separate storage area for additional processing, executing a control function to make adjustments (e.g., perform an update, a rollback, etc.) to place the first program code in a better state for execution, etc. In some embodiments, the second program code may be configured to modify at least one of the first program code or the configuration data associated with the first program code. For example, if the frontend 120 determined that the amount of memory allocated for executing the first program code is too low and is causing the code execution requests associated with the first program code to fail, the frontend 120 may cause the second program code to be executed, where the second program code is configured to increase the amount of memory allocated for executing the first program code. In another example, the second program code may be configured to send the request, after the request has failed to process successfully, to a separate storage area (e.g., a dynamic DLQ) configured to store requests for further analysis/processing. The requests that are sent to such a separate storage area may be sent back to the frontend 120 for re-processing, may generate notifications to the account owner, or may be discarded. A process for sending a request to a DLQ is described in greater detail below with reference to FIG. 4.

While the routine 300 of FIG. 3 has been described above with reference to blocks 302-310, the embodiments described herein are not limited as such, and one or more blocks may be omitted, modified, or switched without departing from the spirit of the present disclosure. Further, the routine 300 of FIG. 3 may include different processes or routines which may be performed in a different order.

Example Routine for Sending Code Execution Requests to DLQ

Turning now to FIG. 4, a routine 400 implemented by one or more components of the virtual compute system 110 (e.g., the frontend 120) will be described. Although routine 400 is described with regard to implementation by the frontend 120, one skilled in the relevant art will appreciate that alternative components may implement routine 400 or that one or more of the blocks may be implemented by a different component or in a distributed manner.

At block 402 of the illustrative routine 400, the frontend 120 receives a code execution request associated with a program code. The code execution requests may arrive at the frontend 120 in the form of an event message that is associated with (or configured to be received by) the program code. For example, such event messages may include an identity of the program code and one or more input parameters to the program code. The program code may be associated with a set of configuration data that is specified for the program code by, for example, the account owner associated with the program code. The configuration data may include the resources allocated for executing the program code. The resources may comprise one or more of CPU, memory, network, input/output (I/O), a maximum number of requests, or a maximum execution duration, or any other computing resources or constraints associated with the program code.

Next, at block 404, the frontend 120 processes the code execution request using the one or more input parameters and the configuration data. For example, the frontend 120 may cause the program code to be executed (e.g., either successfully or unsuccessfully) using the input parameters and the configuration data associated with the program code.

At block 406, the frontend 120 determines whether the execution of the program code (or the attempted execution thereof) has produced an unexpected result. The unexpected result may be any one of an error, a warning, or any result other than a successful execution of the program code. If the frontend 120 determines that the execution of the program code has not produced an unexpected result, the routine proceeds to block 402 to receive and process additional requests. Otherwise, the routine 400 proceeds to block 408.

At block 408, the frontend 120 determines whether the request should be sent to a DLQ (or any other separate storage area that is either internal or external to the virtual compute system 110). If the frontend 120 determines that the request should not be sent to a DLQ, the routine 400 proceeds to block 410 to perform an alternative action. The alternative action may include, for example, any one of creating a log detailing the unexpected result, automatically modifying the program code or the configuration data associated with the program code, sending a warning message or notification to the account owner, executing a control function to make adjustments (e.g., perform an update, a rollback, etc.) to place the first program code in a better state for execution, discarding the request, etc. If the frontend 120 determines that the request should be sent to a DLQ, the routine 400 proceeds to block 412 to send the request to the DLQ.

At block 414, the frontend 120 determines whether a request stored in the DLQ should be re-processed. For example, the request considered for re-processing may be the request that was sent to the DLQ at block 412. In another example, the request considered for re-processing may be a different request that already existed in the DLQ with the request was sent to the DLQ at block 412. In some embodiments, the frontend 120 performs other actions or processes other jobs between blocks 412 and 414. For example, after a request is sent to the DLQ at block 412, the same request may be considered for re-processing at block 414 after a threshold amount of time (e.g., 5 minutes, 30 minutes, 2 hours, etc.) has passed. If the frontend 120 determines that the request stored in the DLQ should be re-processed, the routine 400 proceeds to block 404 to process the request. Otherwise, the frontend 120 performs an alternative action at block 416. For example, the alternative action may be any one of creating a log detailing the unexpected result, automatically modifying the program code or the configuration data associated with the program code, sending a warning message or notification to the account owner, executing a control function to make adjustments (e.g., perform an update, a rollback, etc.) to place the first program code in a better state for execution, discarding the request, etc. Although several actions that may be performed by the frontend 120 are described herein, the actions that may be performed by the frontend 120 in response to detecting an unexpected results are not limited to such examples, and may include other actions.

While the routine 400 of FIG. 4 has been described above with reference to blocks 402-416, the embodiments described herein are not limited as such, and one or more blocks may be omitted, modified, or switched without departing from the spirit of the present disclosure. Further, the routine 400 of FIG. 4 may include different processes or routines which may be performed in a different order.

Other Considerations

It will be appreciated by those skilled in the art and others that all of the functions described in this disclosure may be embodied in software executed by one or more physical processors of the disclosed components and mobile communication devices. The software may be persistently stored in any type of non-volatile storage.

Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements 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.

Any process 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 steps in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art. It will further be appreciated that the data and/or components described above may be stored on a computer-readable storage medium and loaded into memory of the computing device using a drive mechanism associated with a computer readable storing the computer executable components such as a CD-ROM, DVD-ROM, or network interface. Further, the component and/or data can be included in a single device or distributed in any manner. Accordingly, general purpose computing devices may be configured to implement the processes, algorithms, and methodology of the present disclosure with the processing and/or execution of the various data and/or components described above.

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, comprising: one or more processors; andone or more memories, the one or more memories having stored thereon instructions, which, when executed by the one or more processors, configure the one or more processors to: receive, from a user computing device over a network, a request to execute a user-provided program code on behalf of a user associated with the user computing device, the request including one or more parameters;cause a virtual machine instance to initiate execution of the user-provided program code based on the one or more parameters included in the request;prior to completion of the execution in response to the request received from the user computing device, modify at least one of the user-provided program code or the one or more parameters included in the request; andsubsequent to the modification of the at least one of the user-provided program code or the one or more parameters, cause the user-provided program code to be executed based on the one or more parameters,wherein the modification comprises modifying the one or more parameters such that an amount of computing resource allocated for executing the user-provided program code is adjusted.
2. The system of claim 1, wherein the modification comprises performing a rollback of the user-provided program code to an older version.
3. The system of claim 1, wherein the modification comprises performing an update of the user-provided program code to a newer version.
4. The system of claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors to cause the request to be stored in a storage area such that the request can be processed again at a later time.
5. The system of claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors to: determine that a first amount of computing resource specified by the one or more parameters is insufficient for executing the user-provided program code; andmodify the one or more parameters to specify a second amount of computing resource greater than the first amount.
6. The system of claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors to throttle requests to execute the user-provided program code such that a rate at which computing resources are used to execute the user-provided program code is decreased.
7. The system of claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors to increase a frequency at which one or more metrics are logged.
8. A computer-implemented method comprising: receiving, from a user computing device over a network, a request to execute a user-provided program code on behalf of a user associated with the user computing device, wherein the user-provided program code is associated with one or more parameters;causing a virtual machine instance to initiate execution of the user-provided program code based on the one or more parameters included in the request;prior to completion of the execution in response to the request received from the user computing device, modifying at least one of the user-provided program code or the one or more parameters included in the request; andsubsequent to the modification of the at least one of the user-provided program code or the one or more parameters, executing the user-provided program code based on the one or more parameters,wherein the modification comprises modifying the one or more parameters such that an amount of computing resource allocated for executing the user-provided program code is adjusted.
9. The computer-implemented method of claim 8, wherein the modification comprises performing a rollback or an update of the user-provided program code to another version.
10. The computer-implemented method of claim 8, further comprising storing the request in a storage area such that the request can be processed again at a later time.
11. The computer-implemented method of claim 8, further comprising: determining that a first amount of computing resource specified by the one or more parameters is insufficient for executing the user-provided program code; andmodifying the one or more parameters to specify a second amount of computing resource greater than the first amount.
12. The computer-implemented method of claim 8, further comprising throttling requests to execute the user-provided program code such that a rate at which computing resources are used to execute the user-provided program code is decreased.
13. Non-transitory physical computer storage storing instructions, which, when executed by one or more computing devices, configure the one or more computing devices to: receive, from a user computing device over a network, a request to execute a user-provided program code on behalf of a user associated with the user computing device, wherein the user-provided program code is associated with one or more parameters;cause a virtual machine instance to initiate execution of the user-provided program code based on the one or more parameters included in the request;prior to completion of the execution in response to the request received from the user computing device, modify at least one of the user-provided program code or the one or more parameters included in the request; andsubsequent to the modification of the at least one of the user-provided program code or the one or more parameters, cause the user-provided program code to be executed based on the one or more parameters,
14. The non-transitory physical computer storage of claim 13, wherein the modification comprises performing a rollback or an update of the user-provided program code to another version.
15. The non-transitory physical computer storage of claim 13, wherein the instructions, when executed by the one or more computing devices, further configure the one or more computing devices to cause the request to be stored in a storage area such that the request can be processed again at a later time.
16. The non-transitory physical computer storage of claim 13, wherein the instructions, when executed by the one or more computing devices, further configure the one or more computing devices to: determine that a first amount of computing resource specified by the one or more parameters is insufficient for executing the user-provided program code; andmodifying the one or more parameters to specify a second amount of computing resource greater than the first amount.
17. The non-transitory physical computer storage of claim 13, wherein the instructions, when executed by the one or more computing devices, further configure the one or more computing devices to throttle requests to execute the user-provided program code such that a rate at which computing resources are used to execute the user-provided program code is decreased.

CROSS-REFERENCE TO OTHER APPLICATIONS

This application is a continuation of U.S. application Ser. No. 14/869,879, filed Sep. 29, 2015 and titled “EVENT-DRIVEN COMPUTING,” the disclosure of which is hereby incorporated by reference in its entirety. The present application's Applicant previously filed the following U.S. patent application on Sep. 30, 2014, the disclosure of which is hereby incorporated by reference in its entirety: application Ser. No.Title14/502,648PROGRAMMATIC EVENT DETECTION AND MESSAGE GENERATION FOR REQUESTS TO EXECUTE PROGRAM CODE

US Referenced Citations (540)

Number	Name	Date	Kind
4949254	Shorter	Aug 1990	A
5283888	Dao et al.	Feb 1994	A
5970488	Crowe et al.	Oct 1999	A
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
6708276	Yarsa et al.	Mar 2004	B1
7036121	Casabona et al.	Apr 2006	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
7886021	Scheifler et al.	Feb 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
8209695	Pruyne et al.	Jun 2012	B1
8219987	Vlaovic et al.	Jul 2012	B1
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
8429282	Ahuja	Apr 2013	B1
8448165	Conover	May 2013	B1
8490088	Tang	Jul 2013	B2
8555281	Van Dijk et al.	Oct 2013	B1
8566835	Wang et al.	Oct 2013	B2
8613070	Borzycki et al.	Dec 2013	B1
8631130	Jackson	Jan 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
8756696	Miller	Jun 2014	B1
8769519	Leitman 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
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
8874952	Tameshige et al.	Oct 2014	B2
8904008	Calder et al.	Dec 2014	B2
8997093	Dimitrov	Mar 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.	Jun 2015	B2
9092837	Bala et al.	Jul 2015	B2
9098528	Wang	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
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
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
9413626	Reque et al.	Aug 2016	B2
9436555	Dornemann et al.	Sep 2016	B2
9461996	Hayton et al.	Oct 2016	B2
9471775	Wagner et al.	Oct 2016	B1
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
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
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
9727725	Wagner et al.	Aug 2017	B2
9733967	Wagner et al.	Aug 2017	B2
9760387	Wagner et al.	Sep 2017	B2
9767271	Ghose	Sep 2017	B2
9787779	Frank et al.	Oct 2017	B2
9811363	Wagner	Nov 2017	B1
9811434	Wagner	Nov 2017	B1
9830175	Wagner	Nov 2017	B1
9830193	Wagner et al.	Nov 2017	B1
9830449	Wagner	Nov 2017	B1
9864636	Patel et al.	Jan 2018	B1
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
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
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
10162672	Wagner et al.	Dec 2018	B2
10162688	Wagner	Dec 2018	B2
10203990	Wagner et al.	Feb 2019	B2
10248467	Wisniewski et al.	Apr 2019	B2
10277708	Wagner et al.	Apr 2019	B2
10303492	Wagner et al.	May 2019	B1
10353678	Wagner	Jul 2019	B1
10353746	Reque et al.	Jul 2019	B2
10365985	Wagner	Jul 2019	B2
10387177	Wagner et al.	Aug 2019	B2
10402231	Marriner et al.	Sep 2019	B2
10437629	Wagner et al.	Oct 2019	B2
10445140	Sagar et al.	Oct 2019	B1
10528390	Brooker et al.	Jan 2020	B2
10552193	Wagner et al.	Feb 2020	B2
10564946	Wagner et al.	Feb 2020	B1
10572375	Wagner	Feb 2020	B1
10592269	Wagner et al.	Mar 2020	B2
10623476	Thompson	Apr 2020	B2
10649749	Brooker et al.	May 2020	B1
10691498	Wagner	Jun 2020	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
20030191795	Bernardin et al.	Oct 2003	A1
20030229794	James, II et al.	Dec 2003	A1
20040003087	Chambliss 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	Feb 2005	A1
20050120160	Plouffe	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
20060080678	Bailey et al.	Apr 2006	A1
20060123066	Jacobs et al.	Jun 2006	A1
20060129684	Datta	Jun 2006	A1
20060184669	Vaidyanathan et al.	Aug 2006	A1
20060200668	Hybre et al.	Sep 2006	A1
20060212332	Jackson	Sep 2006	A1
20060242647	Kimbrel et al.	Oct 2006	A1
20060248195	Toumura et al.	Nov 2006	A1
20070033085	Johnson	Feb 2007	A1
20070094396	Takano et al.	Apr 2007	A1
20070130341	Ma	Jun 2007	A1
20070174419	O'Connell et al.	Jul 2007	A1
20070192082	Gaos et al.	Aug 2007	A1
20070199000	Shekhel et al.	Aug 2007	A1
20070220009	Morris 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
20090006897	Sarsfield	Jan 2009	A1
20090013153	Hilton	Jan 2009	A1
20090025009	Brunswig et al.	Jan 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
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
20090300599	Piotrowski	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
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
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
20100269109	Cartales	Oct 2010	A1
20100312871	Desantis et al.	Dec 2010	A1
20100325727	Neystadt et al.	Dec 2010	A1
20110010722	Matsuyama	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
20110153727	Li	Jun 2011	A1
20110153838	Belkine et al.	Jun 2011	A1
20110154353	Theroux et al.	Jun 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
20110265164	Lucovsky	Oct 2011	A1
20110271276	Ashok	Nov 2011	A1
20110276945	Chasman et al.	Nov 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
20120072762	Atchison et al.	Mar 2012	A1
20120072914	Ota	Mar 2012	A1
20120079004	Herman	Mar 2012	A1
20120096271	Ramarathinam et al.	Apr 2012	A1
20120096468	Chakravorty et al.	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
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
20120222038	Katragadda et al.	Aug 2012	A1
20120233464	Miller et al.	Sep 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
20130061220	Gnanasambandam et al.	Mar 2013	A1
20130067494	Srour et al.	Mar 2013	A1
20130080641	Lui et al.	Mar 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
20130139152	Chang et al.	May 2013	A1
20130139166	Zhang et al.	May 2013	A1
20130151648	Luna	Jun 2013	A1
20130152047	Moorthi 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	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
20130232480	Winterfeldt et al.	Sep 2013	A1
20130239125	Iorio	Sep 2013	A1
20130262556	Xu et al.	Oct 2013	A1
20130263117	Konik 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
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
20130311650	Brandwine et al.	Nov 2013	A1
20130326506	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
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
20140059209	Alnoor	Feb 2014	A1
20140059226	Messerli et al.	Feb 2014	A1
20140059552	Cunningham et al.	Feb 2014	A1
20140068568	Wisnovsky	Mar 2014	A1
20140068611	McGrath 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
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
20140201735	Kannan et al.	Jul 2014	A1
20140207912	Thibeault et al.	Jul 2014	A1
20140215073	Dow et al.	Jul 2014	A1
20140229221	Shih et al.	Aug 2014	A1
20140245297	Hackett	Aug 2014	A1
20140279581	Devereaux	Sep 2014	A1
20140280325	Krishnamurthy 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
20140304698	Chigurapati et al.	Oct 2014	A1
20140304815	Maeda	Oct 2014	A1
20140317617	O'Donnell	Oct 2014	A1
20140344457	Bruno, Jr. et al.	Nov 2014	A1
20140344736	Ryman et al.	Nov 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
20150052258	Johnson et al.	Feb 2015	A1
20150058914	Yadav	Feb 2015	A1
20150067830	Johansson et al.	Mar 2015	A1
20150074659	Madsen et al.	Mar 2015	A1
20150081885	Thomas et al.	Mar 2015	A1
20150106805	Melander et al.	Apr 2015	A1
20150120928	Gummaraju et al.	Apr 2015	A1
20150134626	Theimer et al.	May 2015	A1
20150135287	Medeiros et al.	May 2015	A1
20150142952	Bragstad et al.	May 2015	A1
20150143381	Chin et al.	May 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
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
20150289220	Kim et al.	Oct 2015	A1
20150309923	Iwata et al.	Oct 2015	A1
20150319160	Ferguson et al.	Nov 2015	A1
20150324229	Valine	Nov 2015	A1
20150332048	Mooring et al.	Nov 2015	A1
20150332195	Jue	Nov 2015	A1
20150350701	Lemus et al.	Dec 2015	A1
20150356294	Tan et al.	Dec 2015	A1
20150363181	Alberti et al.	Dec 2015	A1
20150370560	Tan 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	Dec 2015	A1
20160011901	Hurwitz et al.	Jan 2016	A1
20160012099	Tuatini et al.	Jan 2016	A1
20160019536	Ortiz et al.	Jan 2016	A1
20160026486	Abdallah	Jan 2016	A1
20160048606	Rubinstein et al.	Feb 2016	A1
20160072727	Leafe et al.	Mar 2016	A1
20160077901	Roth et al.	Mar 2016	A1
20160098285	Davis et al.	Apr 2016	A1
20160100036	Lo et al.	Apr 2016	A1
20160117254	Susarla et al.	Apr 2016	A1
20160124665	Jain et al.	May 2016	A1
20160140180	Park et al.	May 2016	A1
20160191420	Nagarajan et al.	Jun 2016	A1
20160212007	Alatorre et al.	Jul 2016	A1
20160285906	Fine et al.	Sep 2016	A1
20160292016	Bussard et al.	Oct 2016	A1
20160294614	Searle et al.	Oct 2016	A1
20160299790	Thompson	Oct 2016	A1
20160301739	Thompson	Oct 2016	A1
20160306613	Busi et al.	Oct 2016	A1
20160350099	Suparna et al.	Dec 2016	A1
20160357536	Firlik et al.	Dec 2016	A1
20160364265	Cao et al.	Dec 2016	A1
20160371127	Antony et al.	Dec 2016	A1
20160371156	Merriman	Dec 2016	A1
20160378449	Khazanchi et al.	Dec 2016	A1
20160378554	Gummaraju et al.	Dec 2016	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
20170085591	Ganda et al.	Mar 2017	A1
20170090961	Wagner et al.	Mar 2017	A1
20170093684	Jayaraman et al.	Mar 2017	A1
20170093920	Ducatel et al.	Mar 2017	A1
20170116051	Wagner et al.	Apr 2017	A1
20170177391	Wagner et al.	Jun 2017	A1
20170177413	Wisniewski et al.	Jun 2017	A1
20170192804	Wagner	Jul 2017	A1
20170199766	Wagner et al.	Jul 2017	A1
20170206116	Reque et al.	Jul 2017	A1
20170230499	Mumick et al.	Aug 2017	A1
20170272462	Kraemer et al.	Sep 2017	A1
20170286143	Wagner et al.	Oct 2017	A1
20170286156	Wagner et al.	Oct 2017	A1
20170371703	Wagner et al.	Dec 2017	A1
20180004553	Wagner et al.	Jan 2018	A1
20180004572	Wagner et al.	Jan 2018	A1
20180039506	Wagner et al.	Feb 2018	A1
20180046453	Nair et al.	Feb 2018	A1
20180046482	Karve et al.	Feb 2018	A1
20180060221	Yim et al.	Mar 2018	A1
20180067841	Mahimkar	Mar 2018	A1
20180121245	Wagner et al.	May 2018	A1
20180143865	Wagner et al.	May 2018	A1
20180157568	Wagner	Jun 2018	A1
20180203717	Wagner et al.	Jul 2018	A1
20180210760	Wisniewski et al.	Jul 2018	A1
20180239636	Arora et al.	Aug 2018	A1
20180253333	Gupta	Sep 2018	A1
20180275987	Vandeputte	Sep 2018	A1
20180309819	Thompson	Oct 2018	A1
20190072529	Andrawes et al.	Mar 2019	A1
20190102231	Wagner	Apr 2019	A1
20190108058	Wagner et al.	Apr 2019	A1
20190155629	Wagner et al.	May 2019	A1
20190171470	Wagner	Jun 2019	A1
20190196884	Wagner	Jun 2019	A1
20190205171	Brooker et al.	Jul 2019	A1
20190227849	Wisniewski et al.	Jul 2019	A1
20190384647	Reque et al.	Dec 2019	A1
20190391834	Mullen et al.	Dec 2019	A1
20190391841	Mullen et al.	Dec 2019	A1
20200057680	Marriner et al.	Feb 2020	A1
20200104198	Hussels et al.	Apr 2020	A1
20200104378	Wagner et al.	Apr 2020	A1
20200192707	Brooker et al.	Jun 2020	A1

Foreign Referenced Citations (33)

Number	Date	Country
2663052	Nov 2013	EP
2002287974	Oct 2002	JP
2006-107599	Apr 2006	JP
2007-538323	Dec 2007	JP
2010-026562	Feb 2010	JP
2011-233146	Nov 2011	JP
2011257847	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
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 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 2020005764	Jan 2020	WO
WO 2020069104	Apr 2020	WO

Non-Patent Literature Citations (75)

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.
Ben-Yehuda et al., “Deconstructing Amazon EC2 Spot Instance Pricing”, ACM Transactions on Economics and Computation 1.3, 2013, 15 pages.
Czajkowski, G., and L. Daynes, Multitasking Without Compromise: A Virtual Machine Evolution 47(4a):60-73, ACM SIGPLAN Notices—Supplemental Issue, Apr. 2012.
Dombrowski, M., et al., Dynamic Monitor Allocation in the Java Virtual Machine,'JTRES '13, Oct. 9-11, 2013, pp. 30-37.
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.
Hoffman, Auto scaling your website with Amazon Web Services (AWS)—Part 2, Cardinalpath, Sep. 2015, 15 pages.
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.
Shim (computing), Wikipedia, https://en.wikipedia.org/w/index.php?title+Shim_(computing)&oldid+654971528, [retrieved on May 26, 2016], 2 pages.
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.
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.
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.
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.
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.
International Search Report and Written Opinion in PCT/US2016/016211 dated Apr. 13, 2016.
International Search Report and Written Opinion in PCT/US2016/026514 dated Jun. 8, 2016.
International Search Report and Written Opinion in PCT/US2016/026520 dated Jul. 5, 2016.
International Search Report and Written Opinion in PCT/US2016/054774 dated Dec. 16, 2016.
International Search Report and Written Opinion in PCT/US2016/066997 dated Mar. 20, 2017.
International Search Report and Written Opinion in PCT/US/2017/023564 dated Jun. 6, 2017.
Amazon, “AWS Lambda: Developer Guide”, Retrieved from the Internet, Jun. 26, 2016, URL : http://docs.aws.amazon.com/lambda/ latest/dg/lambda-dg.pdf.
Balazinska et al., Moirae: History-Enhanced Monitoring, Published: 2007, 12 pages.
Bhadani et al., Performance evaluation of web servers using central load balancing policy over virtual machines on cloud, Jan. 2010, 4 pages.
Das et al., Adaptive Stream Processing using Dynamic Batch Sizing, 2014, 13 pages.
Deis, Container, 2014, 1 page.
Han et al., Lightweight Resource Scaling for Cloud Applications, 2012, 8 pages.
Kamga et al., Extended scheduler for efficient frequency scaling in virtualized systems, Jul. 2012, 8 pages.
Kazempour et al., AASH: an asymmetry-aware scheduler for hypervisors, Jul. 2010, 12 pages.
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.
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, 2013, <hal-01228236, pp. 81-89.
Stack Overflow, Creating a database connection pool, 2009, 4 pages.
Tan et al., Provisioning for large scale cloud computing services, Jun. 2012, 2 pages.
Yamasaki et al, “Model-based resource selection for efficient virtual cluster deployment”, Virtualization Technology in Distributed Computing, ACM, Nov. 2007, pp. 1-7.
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 2012.
Extended Search Report in European Application No. dated May 3, 2018.
International Preliminary Report on Patentability in PCT/US2016/016211 dated Aug. 17, 2017.
International Preliminary Report on Patentability in PCT/US2016/026514 dated Oct. 10, 2017.
International Preliminary Report on Patentability in PCT/US2016/026520 dated Oct. 10, 2017.
International Preliminary Report on Patentability in PCT/US2016/054774 dated Apr. 3, 2018.
International Preliminary Report on Patentability in PCT/US2016/066997 dated Jun. 26, 2018.
International Search Report and Written Opinion in PCT/US2017/040054 dated Sep. 21, 2017.
International Search Report and Written Opinion in PCT/US2017/039514 dated Oct. 10, 2017.
Amazon, “AWS Lambda: Developer Guide”, Retrieved from the Internet, Apr. 30, 2016, URL: http://docs.aws.amazon.com/lambda/ latest/dg/lambda-dg.pdf., 346 pages.
Extended Search Report in European Application No. 15847202.7 dated Sep. 9, 2018.
Extended Search Report in European Application No. 15846542.7 dated Aug. 27, 2018.
International Preliminary Report on Patentability in PCT/US/2017/023564 dated Oct. 2, 2018.
CodeChef ADMIN discussion web page, retrieved from https://discuss.codechef.com/t/what-are-the-memory-limit-and-stack-size-on-codechef/14159, 2019.
CodeChef IDE web page, Code, Compile & Run, retrieved from https://www.codechef.com/ide, 2019.
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.
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,va123, 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.
Sakamoto, et al. “Platform for Web Services using Proxy Server”; Research Report from Information Processing Society, Mar. 22, 2002, vol. 2002, No. 31.
Wang et al., “Improving utilization through dynamic VM resource allocation in hybrid cloud environment”, Parallel and Distributed V Systems (ICPADS), IEEE, 2014. Retrieved on Feb. 14, 2019, Retrieved from the internet: URL<https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7097814, 8 pages.
Wikipedia List_of_HTTP status_codes web page, retrieved from https;//en.wikipedia.org/wiki/List_of_HTTP status_codes, 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, 2019.
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.
International Preliminary Report on Patentability in PCT/US2017/040054 dated Jan. 1, 2019.
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.
Tange, “GNU Parallel: The Command-Line Power Tool”, vol. 36, No. 1, Jan. 1, 1942, pp. 42-47.
Wikipedia “API” pages from date Apr. 7, 2015, retrieved using the WayBackMachine from https://web.archive.org/web/20150407191158/https://en.wildpedia.org/wiki/Application_programming_interface.
Extended Search Report in European Application No. 19199402.9 dated Mar. 6, 2020.
Office Action in European Application No. 17743108.7 dated Jan. 14, 2020.

Related Publications (1)

	Number	Date	Country
	20170371724 A1	Dec 2017	US

Continuations (1)

	Number	Date	Country
Parent	14869879	Sep 2015	US
Child	15595774		US

Event-driven computing

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