Patent Grant
11550713
Computing devices can utilize communication networks to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or to provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as a “data center,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public.
To facilitate increased utilization of data center resources, virtualization technologies allow a single physical computing device to host one or more instances of virtual machines that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical computing device can create, maintain, delete, or otherwise manage virtual machines in a dynamic manner. In turn, users can request computer resources from a data center, including single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources.
Virtual machines are typically defined at least partly based on the data used to run the virtual machine, which is often packaged into a disk image. Generally described, a disk image is data set, such as a file, that contains the contents and structure of a disk volume or data storage device. For example, a disk image may contain an operating system, libraries, utilities, applications, configurations, and the like. By generating a virtual machine and provisioning it with a disk that matches the contents of the disk image, a user may configure the virtual machine to implement desired functionality. Disk images are also utilized in other virtualization techniques, such as operating-system-level virtualization, a technique in which the kernel of an operating system enables multiple isolated user space instances (often called “containers”) without requiring virtualization of the kernel.
Generally described, aspects of the present disclosure relate to facilitating execution of code on a serverless code execution system, which may also be referred to as an on-demand code execution system. As described herein, a serverless code execution system enables rapid execution of source code, which may be supplied by users of the on-demand code execution system. For example, a user may submit a script in a specific programming language (e.g., the PYTHON™ language) that, when executed, implements network-based processing for a user-facing application (e.g., a mobile device “app”). The serverless code execution system can then enable the user to submit “calls” to execute that script, at which point the system will securely execute the script to provide the desired functionality. Unlike some other network-based services, a serverless code execution system can remove the need for a user to maintain or configure a computing device, either virtual or physical, to support code execution. It is this lack of need for a user to maintain a device that leads to the “serverless” moniker, though of course the serverless code execution system itself, as opposed to individual users, likely maintains servers to support code execution. Serverless systems can be particularly well suited for processes with varying demand, as the serverless system can rapidly scale up and down the computing resources used to service such processes. In comparison to traditional systems using dedicated servers (physical or virtual), serverless systems often provide much higher efficiency with respect to computing resources used.
One challenge in serverless code execution systems is rapid provisioning of an execution environment (such as a virtual machine instance or software container) to support code execution. One approach is to await calls to execute a set of code, and in response to that call, to generate an execution environment for the code, provision the environment with the code, and execute the code. While effective, this approach can introduce significant latency into request handling, particularly as compared to a server that is pre-provisioned. For even moderately latency sensitive workloads, this approach may render serverless computing infeasible. Another approach is to pre-create environments on the serverless system and pre-provision those environments with all necessary data (e.g., operating systems, runtimes, libraries, etc.) to service any possible request to execute code. However, this approach largely negates the efficiency gains of serverless computing, and where a large amount of code is supported, may quickly overwhelm the resources of a serverless code execution system. Middling approaches, such as where a serverless code execution system attempts to predict future calls to execute code and to pre-provision environments for those calls, are possible. However, predicting future calls is difficult and generally inaccurate, leading either to excessive resource usage or excessive latency.
The scale of the above-noted problems is in many cases proportional to the size of data required to support execution of code. For code that depends on relatively small sets of data (e.g., on the order of kilobytes, single megabytes, tens of megabytes, etc.), the latency to provision environments “on-demand” (in response to a request to execute code) may be acceptable to an end user. Similarly, the computing resources needed to maintain a pre-provisioned environment for such a set of code may be minimal. However, many end users may desire to execute code that depends on larger sets of data. For example, an end user may desire to create a disk image that supports execution of code, including for example an operating system, runtime, libraries, the code itself, configuration files, or the like. On example of such a disk image is an image conforming to the Open Container Initiative (OCI) Image Specification, which is known in the art. Because of the type of data contained in such a disk image (e.g., a complete set of data facilitating execution of code, including an operating system), these images can be relatively large; often on the order of gigabytes in size. Attempting to either pre-provision environments with a large number of such images would quickly overwhelm many computing systems, while a naïve approach to on-demand provisioning of environments with such images, such as by transferring the whole image across a network to a device prior to executing the code, would introduce significant latency.
Embodiments of the present disclosure address these problems by providing for more efficient on-demand provisioning of environments with large data sets, such as disk images supporting code execution. More specifically, embodiments of the present disclosure enable “lazily” loading large data sets into an execution environment, by quickly providing a minimum portion of a data set needed to being execution of code, and providing additional portions of the data set on-demand from the code execution. More specifically, a request to execute code may be satisfied by provisioning an execution environment with access to a data set, without actually transferring the entire data set to a local storage drive for the environment. Instead, the data set may be made available via a file system that operates to selectively retrieve portions of the data set as they are read by the code execution. For example, a host computing device that is hosting an execution environment may be configured to provide a Filesystem in User Space (FUSE) storage device that—from the view of the execution environment—contains the data set. On reads to the FUSE storage device, a local FUSE agent may selectively retrieve any required portions of the data set and make the read portion of the data set available on the FUSE storage device. Thus, from the point of view of the execution environment, complete local access to the data set is provided. However, because the data set is “lazily” loaded, code execution can begin before the entire data set is transferred to the execution environment. Indeed, if the environment never requires access to a portion of the data set, that portion need never be transferred to the environment. As such, the latency to execute code is reduced.
Generally, transferring a portion of a data set to an execution environment can include transferring that data over a network. For example, the data set may be stored in a (logically) centralized network storage service, and portions of a data set may be transferred on-demand to an execution environment as code within that environment reads the data set. To minimize network latency, embodiments of the present disclosure can utilize a multi-level caching system for portions of data sets. For example, a host device hosting execution environments may provide a first level cache, such that recently read portions of data sets are stored on a storage drive of the host device and thus subsequent reads of those portions can be handled locally without network transfer. One or more distributed level two caching devices can provide a second level cache, whereby portions of data sets read by multiple execution environments among multiple host devices are stored within the second level cache. The second level cache may be closer to the host devices than the centralized network storage service, and/or have a network connection to the host devices that has more bandwidth than a connection between the host devices and the centralized network storage service. Thus, portions of data sets that are have not been read recently enough to be stored in a host-local cache may nevertheless be stored in the second level cache, enabling an execution environment to more quickly access those portions. In this configuration, the network storage service may act as a “origin” server for portions of the data set, such that if a portion exists in neither the first nor the second level cache, it can be retrieved from the network storage service.
While caching can improve performance of commonly-used data sets, latency may nevertheless be incurred due to “cache misses”—instances in which a read portion of a data set does not exist in either a local or level two cache. Such cache misses may be especially prevalent when data sets of different users are treated as distinct. In this configuration, it might be expected that only frequently executed code is associated with a cached data set, and that execution of other code would incur frequent cache misses to retrieve their associated data sets. Thus, to reduce the number of cache misses, it may be desirable to provide for sharing of portions between data sets. Typical disk imaging mechanisms do not provide for such sharing, or do so only in a limited fashion. To increase the number of shared portions among data sets, embodiments of the present disclosure may utilize the techniques disclosed in U.S. patent application Ser. No. 17/037,369, filed on Sep. 29, 2020 and entitled “EFFICIENT DEDUPLICATION USING BLOCK-BASED CONVERGENT ENCRYPTION” (the “′369 Application” the entirety of which is hereby incorporated by reference. As disclosed in more detail therein, each data set (e.g., disk image) may be divided into a set of portions (referred to in the '369 Application as “blocks” and generally referred to herein as portions or “objects”) and encrypted using a convergent encryption process, such that if two portions of different data sets contain the same data, they result in identical encrypted portions. Each portion may additionally be identified according to a unique identifier derivable from the portion (and potentially other data), such as a hash value of the portion or message authentication code (MAC) for the portion. These portions may be of a fixed size, such as 512 kilobytes, across all data sets handled by a serverless code execution system. Accordingly, when two data sets provided to the serverless code execution system overlap in at least one fixed-size portion, that portion can be treated as shared. As such, when a first code execution attempts to read the shared portion, it may be cached such that other code executions may (if they are also associated with a data set that includes the shared portion) read the portion from the cache. Sharing of portions among code executions can therefore significantly reduce cache misses, decreasing the latency associated with a code execution reading data from a data set.
Another potential cause for latency when “lazily” transferring portions of data sets is the potential for failures or delays within the level two cache. For example, a device providing the level two cache may fail, meaning that attempts to retrieve a portion from that device would also fail. Even absent outright failure, such a device may experience partial failures or slowdowns that significantly delay transfer of a requested portion. Embodiments of this disclosure can provide a level two cache configured to overcome these problems by distributing data set portions among multiple devices within the level two cache. More specifically, embodiments of the present disclosure can utilize the technique of erasure coding (a known technique in the art) to divide a data set portion into a number of erasure-coded parts.
In accordance with known techniques of erasure coding, data (e.g., a file) can be divided into multiple parts, and reconstructed so long as a threshold number of those parts are known. For example, in a “5/1” erasure coding schema, data is divided into five parts and can be reconstructed so long as no more than one part is lost. An illustrative example of erasure coding is the use of a parity part. For example, data may be divided into two equally sized parts, and a third part may be constructed with each bit of the third part being a “exclusive or” (XOR) value of the corresponding bits of the respective first and second parts. In this example, loss of any single part can be tolerated, as the bits for the missing parts can be calculated from the values of the remaining two parts. A variety of more complex erasure coding techniques are known, enabling specification of a wide variety of “sizes” (e.g., number of parts) and loss tolerance thresholds.
In embodiments of the present disclosure, each portion of a disk image may be divided into a number of parts via erasure coding, which parts are then distributed among devices providing a level two cache. In this way, failures among the level two cache are tolerated up to the loss tolerance threshold of the erasure coding used. Moreover, this technique provides for improvements with respect to slowdowns as well as outright failures. Specifically, a host device may query a level two cache system for all parts of a requested portion, but begin to reconstruct the portion as soon as the minimum number of parts (the total number minus the loss tolerance threshold) are retrieved. In the instance that retrieval of parts experiences a “long tail” distribution (where one or more parts takes much longer to retrieve), this technique enables a host device to “cut off” that tail, servicing the request without delay due to the slower parts.
While examples above are provided with respect to disk images, embodiments of the present disclosure may be utilized to provide any number of data sets. For example, a serverless code execution system may in some instances utilize virtual machine snapshots (e.g., storing a state of random access memory (RAM), central processing unit (CPU) registers, etc.) to record a state of a virtual machine instance at a time that the instance is initialized to execute code. The serverless code execution system may then service requests to execute code by using these snapshots to recreate the virtual machine instance as reflected in the snapshot, which may potentially avoid delays such as booting an operating system. Illustrative techniques for use of snapshots to support rapid execution of code on a serverless code execution system are disclosed, for example, in U.S. patent application Ser. No. 16/045,593, filed Jul. 25, 2018 and entitled “REDUCING EXECUTION TIMES IN AN ON-DEMAND CODE NETWORK CODE EXECUTION SYSTEM USING SAVED MACHINE STATES” (the “′593 Application”), the entirety of which is hereby incorporated by reference. The techniques described herein may be utilized to provide such snapshots to execution environments, in addition or alternatively to disk images. Other types of data set may also be distributed using the techniques described herein. Thus, reference to a disk image as an example data set is intended for illustrative purposes.
Another problem that may occur when distributing data sets is that of garbage collection. In accordance with the above description, embodiments of the present disclosure may generate, for a given data set provided by an end user to support code execution, significant additional data. For example, a disk image may be divided into a number of portions stored on a network storage system. The disk image may further be used to generate a virtual machine snapshot, which snapshot may similarly be divided into portions stored on a network storage system. In the instance that the original data set is maintained, these disk image portions and snapshot portions may be viewed as additional data that support rapid execution of code, but are not strictly necessary to execute that code. Because the number of data sets (including disk images and snapshots) maintained by a serverless code execution system may be large, it may be desirable to limit the number of disk image portions or snapshot portions maintained on the network storage system. In a similar manner to traditional caching, for example, it may be desirable to maintain only disk image portions or snapshot portions that recently supported code execution, while deleting those portions that have not recently supported code execution. This process of deleting not-recently-used portions is referred to herein as “garbage collection.”
While described in a simple manner above, garbage collection within a network storage system may in practice be a difficult problem. To support storage of a large volume of data, the network storage system may be distributed among multiple devices. A well-known issue in such distributed systems is that of “reference counting”—knowing how many processes rely on a specific piece of data at any given time. Typically, if a process relies on data, it is undesirable to garbage collect that data. However, the shared nature of data set (e.g., disk image or snapshot) portions used by present embodiments makes reference counting with respect to such portions difficult. For example, a process may communicate with each relevant device in the distributed system to detect that a given portion has not been accessed in a threshold period of time, and therefore may delete that portion. Unbeknownst to that process, a separate process may—during the data gathering of the first process—use the portion. Thus, deletion by the first process would result in an error to the second process. Accordingly, fine-grained usage tracking, such as at a portion level, may result in errors.
Embodiments of the present disclosure address this issue by providing coarse-grained garbage collection, in a manner that minimizes potential for errors in a serverless code execution system while still enabling efficient garbage collection. More specifically, a network storage system may store data set portions in a number of logically divided partitions, referred to herein as “roots” (as they represent a logical “root” object structure under which portions may be stored). Each root may undergo a lifecycle, beginning as an active root, to which new portions can be written, and later transitioning to an inactive root that does not support writing of portions. Transitioning between active and inactive may occur periodically, such as on a fixed time scale (e.g., within a few days, a week, two weeks, etc.), with new active roots created to replace those transitioning to an inactive state. Each newly created portion can be placed into an active root, from which the portion can later be read to support code execution. When that root is later transitioned to an inactive state, it may (at least initially) still support reading of the portion. However, on reading a portion from an inactive root, a migration process may also copy the portion into another active root, and further execution environments reliant on that portion can be configured to retrieve the portion from the active root. After a sufficient period of time without supporting a read (e.g., to designate a data set as subject to garbage collection), an inactive root may then be deleted, thus reclaiming resources used to store portions in the inactive root. Because reading from a root may pause the deletion process, the issue of reference counting is substantially reduced or eliminated. Moreover, because reading from an inactive root causes a portion to be copied to an active root, and subsequent environments to read from the active root, this technique enables unused portions to collect within inactive roots and be subject to garbage collection, while commonly used portions are continuously sifted out and “dragged forward” into each subsequent active root. Accordingly, this technique can provide for coarse-grained garbage collection that solves the problems associated with fine-grained garbage collection described above.
As will be appreciated by one of skill in the art in light of the present disclosure, the embodiments disclosed herein improve the ability of computing systems, such as serverless code execution systems, to support rapid execution of code reliant on a data set. Moreover, the presently disclosed embodiments address technical problems inherent within computing systems; specifically, the limited nature of computing resources available to store data sets and the difficulty of rapidly providing required data to code executions, when the variety of potential data and code executions is large. These technical problems are addressed by the various technical solutions described herein, including providing for “lazy,” on-demand retrieval of data set portions that may be shared among multiple code executions, providing for a level two cache that utilizes erasure coding to provide resiliency and reduced request latency, and providing for a network storage system that implements coarse-grained garbage collection at the level of life-cycled logical storage partitions (“roots”). Thus, the present disclosure represents an improvement on serverless code execution systems and computing systems in general.
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 description, when taken in conjunction with the accompanying drawings.
The illustrative environment 100 further includes one or more auxiliary services 106, which can interact with the serverless code execution environment 110 to implement desired functionality on behalf of a user. Auxiliary services 106 can correspond to network-connected computing devices, such as servers, which generate data accessible to the serverless code execution environment 110 or otherwise communicate to the serverless code execution environment 110. For example, the auxiliary services 106 can include web services (e.g., associated with the user computing devices 102, with the serverless code execution system 110, or with third parties), databases, really simple syndication (“RSS”) readers, social networking sites, or any other source of network-accessible service or data source. In some instances, auxiliary services 106 may be invoked by code execution on the serverless code execution system 110, such as by API calls to the auxiliary services 106. In some instances, auxiliary services 106 may be associated with the serverless code execution system 110, e.g., to provide billing or logging services to the serverless code execution system 110. In some instances, auxiliary services 106 actively transmit information, such as API calls or other task-triggering information, to the serverless code execution system 110. In other instances, auxiliary services 106 may be passive, such that data is made available for access by the serverless code execution system 110. For example, components of the serverless code execution system 110 may periodically poll such passive data sources, and trigger execution of code within the serverless code execution system 110 based on the data provided. While depicted in
The client devices 102, auxiliary services 106, and serverless code execution system 110 may communicate via a network 104, which may include any wired network, wireless network, or combination thereof. For example, the network 104 may be a personal area network, local area network, wide area network, over-the-air broadcast network (e.g., for radio or television), cable network, satellite network, cellular telephone network, or combination thereof. As a further example, the network 104 may be a publicly accessible network of linked networks, possibly operated by various distinct parties, such as the Internet. In some embodiments, the network 104 may be a private or semi-private network, such as a corporate or university intranet. The network 104 may include one or more wireless networks, such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Long Term Evolution (LTE) network, or any other type of wireless network. The network 104 can use protocols and components for communicating via the Internet or any of the other aforementioned types of networks. For example, the protocols used by the network 104 may include Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), Message Queue Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and the like. Protocols and components for communicating via the Internet or any of the other aforementioned types of communication networks are well known to those skilled in the art and, thus, are not described in more detail herein.
The serverless code execution system 110 is depicted in
Further, the serverless code execution system 110 may be implemented directly in hardware or software executed by hardware devices and may, for instance, include one or more physical or virtual servers implemented on physical computer hardware configured to execute computer executable instructions for performing various features that will be described herein. The one or more servers may be geographically dispersed or geographically co-located, for instance, in one or more data centers. In some instances, the one or more servers may operate as part of a system of rapidly provisioned and released computing resources, often referred to as a “cloud computing environment.”
In the example of
In
To enable interaction with the serverless code execution system 110, the system 110 includes multiple frontends 120, which enable interaction with the serverless code execution system 110. In an illustrative embodiment, the frontends 120 serve as a “front door” to the other services provided by the serverless code execution system 110, enabling users (via user computing devices 102) to provide, request execution of, and view results of computer executable source code. The frontends 120 include a variety of components to enable interaction between the serverless code execution system 110 and other computing devices. For example, each frontend 120 may include a request interface providing user computing devices 102 with the ability to upload or otherwise communication user-specified code and associated data sets to the on-demand code execution system 110 (e.g., in the form of a disk image) and to thereafter request execution of that code. In one embodiment, the request interface communicates with external computing devices (e.g., user computing devices 102, auxiliary services 106, etc.) via a graphical user interface (GUI), CLI, or API. The frontends 120 process the requests and makes sure that the requests are properly authorized. For example, the frontends 120 may determine whether the user associated with the request is authorized to access the source code specified in the request.
References to source code as used herein may refer to any program code (e.g., a program, routine, subroutine, thread, etc.) written in a specific program language. In the present disclosure, the terms “source code,” “user code,” and “program code,” may be used interchangeably. Source code which has been compiled for execution on a specific device is generally referred to herein as “machine code.” Both “source code” and “machine code” are representations of the same instructions, which may be collectively referred to as “code.” Such code may be executed to achieve a specific function, for example, in connection with a particular web application or mobile application developed by the user. As noted above, individual collections of code (e.g., to achieve a specific function) are referred to herein as “tasks” or “functions,” while specific executions of that code are referred to as “task executions,” “function executions,” “code executions,” or simply “executions.” Source code for a task may be written, by way of non-limiting example, in JavaScript (e.g., node.js), Java, Python, and/or Ruby (and/or another programming language). Tasks may be “triggered” for execution on the serverless code execution system 110 in a variety of manners. In one embodiment, a user or other computing device may transmit a request to execute a task may, which can generally be referred to as “call” to execute of the task (e.g., a “task call,” a “function call,” etc.). Such calls may include an identifier of the task to be executed and one or more arguments to be used for executing the task. A request interface of the frontend 120 may receive calls to execute tasks as Hypertext Transfer Protocol Secure (HTTPS) requests from a user. Also, any information (e.g., headers and parameters) included in the HTTPS request may also be processed and utilized when executing a task. As discussed above, any other protocols, including, for example, HTTP, MQTT, and CoAP, may be used to transfer the message containing a task call to the request interface.
Prior to calling for execution of a task, an end user may subject code for the task and associated data to be used to execute the task. In one embodiment, the code is provided in the form of a disk image containing the code and other data that the code may use during execution. The disk image and associated metadata for the task (e.g., the end user who “owns” the task or other information regarding the task) may be stored within an object storage system 190. The object storage system 190 of
Additionally or alternatively, roots 194 of the object storage service 190 may be used to store other data set portions, such as portions representing a snapshot of a virtual machine instance at a particular point in time (e.g., when initialized to support execution of corresponding code). Creation of such snapshots is discussed in more detail in the ′593 Application, incorporated by reference above. Portions for such snapshots may be created, for example, according to the techniques of the ′369 Application when applied to such snapshots as an input data set.
In accordance with the teachings of the ′369 Application, each data set may be represented in the object storage system 190 as a combination of portions, as well as a manifest that lists the combination of portions that collectively represent that data set. For example, each data set may be associated with a manifest that lists a set of identifiers for data set portions (e.g., “chunks”), such that a device with access to the manifest can retrieve the chunks and recreate the data set. In embodiments where portions are encrypted, a manifest can further include information enabling decryption of those portions, such as the encryption key by which each portion was encrypted. In one embodiment, manifests are stored alongside portions within a given root 194. In another embodiment, manifests are stored separately on the object storage system 190.
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While not shown in
After a user has created a task on the serverless code execution system 110, the system 110 may accept calls to execute that task. To calls to execute a task, the frontend 120 can include an execution queue, which can maintain a record of requested task executions. Illustratively, the number of simultaneous task executions by the serverless code execution system 110 is limited, and as such, new task executions initiated at the serverless code execution system 110 (e.g., via an API call, via a call from an executed or executing task, etc.) may be placed on the execution queue and processed, e.g., in a first-in-first-out order. In some embodiments, the on-demand code execution system 110 may include multiple execution queues, such as individual execution queues for each user account. For example, users of the serverless code execution system 110 may desire to limit the rate of task executions on the serverless code execution system 110 (e.g., for cost reasons). Thus, the serverless code execution system 110 may utilize an account-specific execution queue to throttle the rate of simultaneous task executions by a specific user account. In some instances, the serverless code execution system 110 may prioritize task executions, such that task executions of specific accounts or of specified priorities bypass or are prioritized within the execution queue. In other instances, the serverless code execution system 110 may execute tasks immediately or substantially immediately after receiving a call for that task, and thus, the execution queue may be omitted.
As noted above, tasks may be triggered for execution at the serverless code execution system 110 based on explicit calls from user computing devices 102 (e.g., as received at a request interface). Alternatively or additionally, tasks may be triggered for execution at the serverless code execution system 110 based on data retrieved from one or more auxiliary services 106w. To facilitate interaction with auxiliary services 106, the frontend 120 can include a polling interface, which operates to poll auxiliary services 106 for data. Illustratively, the polling interface may periodically transmit a request to one or more user-specified auxiliary services 106 to retrieve any newly available data (e.g., social network “posts,” news articles, files, records, etc.), and to determine whether that data corresponds to user-established criteria triggering execution a task on the serverless code execution system 110. Illustratively, criteria for execution of a task may include, but is not limited to, whether new data is available at the auxiliary services 106, the type or content of the data, or timing information corresponding to the data. In some instances, the auxiliary services 106 may function to notify the frontend 120 of the availability of new data, and thus the polling service may be unnecessary with respect to such services.
In addition to tasks executed based on explicit user calls and data from auxiliary services 106, the serverless code execution system 110 may in some instances operate to trigger execution of tasks independently. For example, the serverless code execution system 110 may operate (based on instructions from a user) to trigger execution of a task at each of a number of specified time intervals (e.g., every 10 minutes).
The frontend 120 can further includes an output interface configured to output information regarding the execution of tasks on the serverless code execution system 110. Illustratively, the output interface may transmit data regarding task executions (e.g., results of a task, errors related to the task execution, or details of the task execution, such as total time required to complete the execution, total data processed via the execution, etc.) to the user computing devices 102 or to auxiliary services 106, which may include, for example, billing or logging services. The output interface may further enable transmission of data, such as service calls, to auxiliary services 106. For example, the output interface may be utilized during execution of a task to transmit an API request to an external service 106 (e.g., to store data generated during execution of the task).
Code executions triggered on the serverless code execution system 110 of
While only a single instance 183 is shown in
To facilitate rapid execution of code, each worker 181 may be configured to maintain a set of instances 183 in a “pre-warmed” state, being at least partially configured to begin execution of code. For example, instances may be created on the worker and configured with access to computing resources (CPU, RAM, drive storage, etc.). In accordance with embodiments of the present disclosure, it may be impractical or impossible to maintain instances 183 in a fully warmed state for all possible code executions, as executions may be associated with a wide variety of at least partially distinct data sets (e.g., disk images and/or snapshots). Thus, instances 183 may be maintained in a “greatest commonality” for a given group of tasks, such as being provisioned with a set of computing resources common to those tasks, being configured to accept an operating system type used by those tasks, etc. \
On receiving instructions to provision an instance 183 to support execution of the task, the worker 181 may adjust the configuration of the instance 183 to support that execution. Specifically, and in accordance with embodiments disclosed herein, the worker 181 may provision the instance 183 with access to a disk image or snapshot corresponding to the task, in a manner that does not require that disk image or snapshot to be fully transferred to local storage of the worker 181 prior to use. Rather, the worker 181 may provide to an instance 183 what appears to be full local access to the disk image or snapshot, while “lazily” retrieving portions of that image or snapshot in response to a request to read such portions. In one embodiment, apparent full local access is provided by a file system process 184, which illustratively represents a FUSE module executing within the user space 182. The file system process 184 may illustratively accept read requests from the instance 183, and interact with a local object manager 188 of the worker 181 to obtain the requested data. To facilitate read requests, the file system process 184 is provided access to an image manifest 186, which illustratively lists a set of portions (e.g., data objects) that collectively represent the disk image or snapshot. For example, the manifest 186 may include a set of identifiers of the portions, a particular root 194 of the object storage system 190 in which the portions are stored, encryption keys by which each portion is encrypted, and information mapping particular logical locations within the disk image or snapshot (e.g., logical block addresses, or “LBAs”) to particular portions. Thus, on receiving a request to read a given range of bytes of a disk image or snapshot, the file system process 184 may, from the request and the manifest 186, identify a particular portion storing the range of bytes, and may request access to that portion from the local object manager 188.
The local object manager 188, in turn, represents code executing on the worker 181 and configured to provide the file system process 184 with access to the requested portion. For example, the local object manager 188 may obtain a request to access a portion, and if the portion is not available within a cache, retrieve that portion from an object root 194 (which root 194 may be identified within the request). On retrieving the portion, the portion may be placed within the object cache 189, which represents “level one” cache of the local object manager 188 (though note the instance 183 itself may implement caches, such as a “page cache” of read data). In one embodiment, the object cache 189 represents a memory-mapped file on a file system of the worker 181, which may be stored for example on high speed storage of the worker 181 to facilitate rapid access by file system processes 184. For example, the object cache 189 may be stored wholly or partly within RAM of the worker 181, and wholly or partly within other high speed storage (e.g., a solid state drive (SSD), 3D XPOINT memory, flash memory, etc.). The object cache 189 may be sized such that it can hold hundreds, thousands, or millions of portions. For example, individual portions may be 512 kb objects, while the cache 189 is hundreds of gigabytes or terabytes in size. On retrieving a requested portion, the local object manager 188 may place the portion into the object cache 189 and return to a requesting file system process 184 a pointer to a location within the cache 189 holding the portion. The process 184 may then read the portion from the location, thus enabling satisfaction of a read request from a VM instance 183.
In one embodiment, each instance 183 is associated with a distinct file system process 184 within its respective user space 182, while each worker 181 includes a single local object manager 188 and object cache 189. Accordingly, multiple instances 183 may gain shared access to the object cache 189. As noted above, multiple data sets of different tasks may overlap with respect to at least some portions. Thus, shared access to the object cache 189 can significantly reduce “cache misses” by enabling a portion retrieved based on a request from one instance 183 to also service requests from another instance 183. For example, where two instances 183 utilize the same operating system, it is likely that a significant percentage of their respective disk images—the portion storing the operating system—overlap. Thus, portions of the disk image would also be expected to overlap, and executions of the two tasks may effectively share access to those portions within the object cache 189. In some instances, the object cache 189 may be “seeded” with commonly used portions prior to execution of any tasks, such as by storing within the cache 189 portions associated with commonly used operating systems, runtimes, libraries, etc. In some instances, these seeded portions may be exempted from cache eviction policies that might otherwise be applied to the cache 189 by the local object manager 188. Portions within the object cache 189 are illustratively maintained as “read only,” such that an instance 183 is unable to modify the portion. Nevertheless, a corresponding disk image or snapshot may in some instances be viewed as writable by instances 183. For example, the file system process 184 may provide the disk image or snapshot using a “copy on write” mechanism, whereby an attempt to write to the disk image or snapshot by the instance 183 causes a modified version of the image or snapshot to be stored in other storage.
The local object manager 188 may, during operation, manage the cache 189 to ensure proper operation. For example, the manager 188 may implement a cache eviction policy, such as deleting one or more least-recently-read portions when storage space of the cache 189 falls below a threshold level. To facilitate cache eviction, the manager 188 may maintain a “reference count” for each portion, indicating a number of instances 183 reading a given portion. For example, each request from a file system process 184 to read a portion may increment a reference count for the portion, while a “close” operation from a process 184 or failure of the process 184 (e.g., a crash) may decrement the reference count. As such, the object manager 188 may maintain information as to which portions are currently in use, in order to facilitate cache eviction.
The file system process 184 and local object manager 188 may communicate via any number of known intra-device techniques. For example, each process 184 may, on initialization, create a Unix socket connection to the manager 188 to facilitate communication.
In addition to the object cache 189 on a given worker 181, the local object manager 188 of
In addition, the system 110 includes a number of components for facilitating distribution of calls to execute a task from frontends 120 to particular VM instances 183. For example, the serverless code execution system 110 includes one or more worker managers 140 configured to manage execution environments (e.g., virtual machine instances) hosted by workers 181 among a worker fleet 180. The worker managers 140—each of which are illustratively implemented as physical or virtual-on-physical devices—illustratively “lease” particular VM instances 183 within the fleet 180, thus gaining operational control to, for example, instruct virtual machine instances 183 to execute code of the task. Thus, on receiving a call to execute a task, a frontend 120 may distribute the call to a worker manager 140, which may identify a currently-leased VM instance 183 in which to implement the task, and cause the instance 183 to implement the task. Example interactions for distributing a call from a frontend 120 to a worker manager 140 are described, for example, in U.S. patent application Ser. No. 16/698,829, entitled “SERVERLESS CALL DISTRIBUTION TO UTILIZE RESERVED CAPACITY WITHOUT INHIBITING SCALING” and filed Nov. 27, 2019, the entirety of which is hereby incorporated by reference.
In the instance that a worker manager 140 does not currently lease a VM instance 183 corresponding to the called task, the worker manager 140 can contact a placement service 160 to request a lease on an additional instance 183, which is illustratively configured to grant to the worker managers 140 leases to individual VM instances 183. Illustratively, the placement service 160 may maintain state information for VM instances 183 across the fleet 180, as well as information indicating which manager 140 has leased a given instance 183. When a worker manager 140 requests a lease on an additional instance 183, the placement service 160 can identify an appropriate instance 183 (e.g., warmed with software and/or data required to support a call to implement a task) and grant to the manager 140 a lease to that instance 183. In the case that such an instance 183 does not exist, the placement service 160 can instruct a worker 181 to create such an instance 183 (e.g., by creating an instance 183 or identifying an existing unused instance 183, storing an appropriate data manifest 186 for a required disk image, snapshot, etc. in a user space 182 of that instance 183, and configuring the file system process 184 to provide access to the required data set) thereafter grant to the worker manager 140 a lease to that instance 183, thus facilitating execution.
In accordance with embodiments of the present disclosure, the placement service 160 may also act to notify the root manager 192 on creation of an instance 183 using a particular data set. For example, the placement service 160 may, when gathering state information indicating currently leased instances 183, identify one or more data sets that such instances 183 rely on, and notify the root manager 192 that such data sets are being accessed. As discussed in more detail below, the root manager 192 may use this information to facilitate copying of data between roots 194 as well as transitioning of roots 194 between life cycle states.
As illustrated, the device 200 includes a processing unit 290, a network interface 292, a computer readable medium drive 294, and an input/output device interface 296, all of which may communicate with one another by way of a communication bus. The network interface 292 may provide connectivity to one or more networks or computing systems. The processing unit 290 may thus receive information and instructions from other computing systems or services via the network 104. The processing unit 290 may also communicate to and from memory 280 and further provide output information for an optional display (not shown) via the input/output device interface 296. The input/output device interface 296 may also accept input from an optional input device (not shown).
The memory 280 may contain computer program instructions (grouped as units in some embodiments) that the processing unit 290 executes in order to implement one or more aspects of the present disclosure, along with data used to facilitate or support such execution. While shown in
The memory 280 may store an operating system 284 that provides computer program instructions for use by the processing unit 290 in the general administration and operation of the device 200. The memory 280 may further include computer program instructions and other information for implementing aspects of the present disclosure. For example, in one embodiment, the memory 280 includes a hypervisor 286 to facilitate creation and management of virtual machine instances 183. While shown as distinct from the operating system 284, the hypervisor 286 and operating system 284 may in some cases be combined. For example, the operating system 284 may be a LINUX operating system executing a Kernel-based Virtual Machine (KVM) virtualization module that acts as the hypervisor 286.
In addition, the memory 280 includes a local object manager 188, which as described above is configured to handle requests from VM instances 183 to read data from a data set, and an object cache 189 representing a set of objects (data set portions) cached locally to the device 200, such as in the form of a memory mapped file. The memory 280 further includes multiple user spaces 182, each of which represents a logically isolated portion of memory 280 associated with a particular VM instance 183. Each user pace 182 illustratively includes VM instance data 288 (data supporting execution of an instance 183), a data manifest 186 that identifies data set portions representing a data set used by a serverless code execution in the instance 183, and a file system process 184 that facilitates interaction between the VM instance 183 and the local object manager 188. In combination, the elements of the memory 280, when executed on the device 200, enable implementation of embodiments of the present disclosure.
The device 200 of
With reference to
The interactions of
At (2), frontend 120 distributes the call to a worker manager 140. The frontend 120 may implement various functionalities to distribute the call, such as selecting the worker manager 140 based on random selection, load, etc. In some instances, the frontend 120 may maintain information identifying a worker manager 140 previously associated with a called task, and distribute the call to that worker manager 140. Various additional functionalities that may be implemented by a frontend 120 to distribute calls to a worker manager 140 are described, for example, in U.S. patent application Ser. No. 16/698,829, entitled “SERVERLESS CALL DISTRIBUTION TO UTILIZE RESERVED CAPACITY WITHOUT INHIBITING SCALING,” which is hereby incorporated by reference in its entirety.
In some instances, the worker manager 140 may determine that an appropriate environment (e.g., a VM instance 183) already exists within the worker fleet 180, and may thus execute an instance of the called task within that environment. However, for purposes of the present description, it will be assumed that no such environment exists. Accordingly, at (3), the manager 140 determines that a new execution environment is required to service the call. The manager 140 therefore, at (4), requests the new environment from the placement service 160.
Thereafter, at (5), the placement service 160 selects an appropriate environment (e.g., from among pre-warmed but not yet leased environments of the fleet 180), and returns a response to the manager 140 at (6). The response to the manager 140 may include, for example, identifying information of the environment, which the manager 140 may utilize to instruct the environment to initiate an execution of the called task. The response may further include information identifying a manifest for a data set to be used to support execution of the task, which may be retrieved, for example, from metadata of the task stored on the system 110 (e.g., in the object storage system 190).
In addition, at (7), the placement service 160 may notify the object storage service 190 (e.g. a root manager 192) that the relevant data set (that used to support execution of the task) is in use. As discussed in more detail below, this notification may be used by the root manager 192 to facilitate garbage collection on the object storage system 190. While
While
On receiving information identifying the execution environment, the worker manager 140, at (8), instructs the worker 181 hosting the environment to execute the called task within the environment. The worker 181, in turn, retrieves from the object storage system 190, at (9), a manifest for the relevant data set (e.g., from a location included within the instructions of the worker manager 140). The worker 181 then at (10) provides the environment with access to the relevant data set, and begins execution of the task within the environment. For example, the worker may modify a VM instance 183 as necessary to match a necessary configuration to support execution of the task, and “mount” the data set as a virtual storage drive of the instance 183, or as a file accessible to the instance 183. As noted above, the data set may be provided in a manner that provides complete local access to the data set, but does not actually require complete transfer of the data set to the worker 181 prior to providing that access. Instead, portions of the data set can be retrieved as they are requested from the VM instance 183. Accordingly, providing the environment with access to the data set is expected to incur significantly lower latency than attempting to completely transfer the data set to the worker 181 prior to execution of the task.
The interactions of
With reference to
The interactions of
At (2), the file system process 184 identifies an object of the object storage system 190 that stores the requested data. As noted above, the file system process 184 can be provided with a manifest that maps locations within the data set (e.g., block offsets) to objects on the object storage system 190, which represent portions of the data set. Thus, by reference to the manifest, the file system process 184 may identify the object. The object may be identified by a name, which illustratively represents a globally unique identifier (GUID) (also known as a universally unique identifier, or UUID) of the object. The name may be generated, for example, as a hash value or media access control (MAC) value of the object. The object may further be identified in the manifest by an object root 194 of the object storage system 190, to facilitate garbage collection on the system 190.
At (3), the file system process 184 then requests data of the object from the local object manager 188 of the worker 181. As noted above, the local object manager 188 can represent a process executing on the worker 181 that facilitates retrieval of objects from a variety of potential sources (e.g., cache levels, an origin, etc.). In one embodiment, the file system process 184 executes within a user space shared with the VM instance 183, while the local object manager 188 executes outside that user space, and the process 184 and manager 188 communicate via a UNIX socket or other intra-device communication system.
At (4), the local object manager 188 loads the requested object into a system of the worker 181, if not already existing therein. Details of this loading are described in more detail below with respect to
As discussed above, the local object manager 188 illustratively provides access to objects to multiple instances 183, and as such an object may be used by more than one instance 183 at a given time. Because space in the local cache is necessarily limited, it may be necessary for the manager 188 to eventually delete one or more objects from the local cache. The manager 188 can therefore be configured to track use of objects by instances 183, to prevent where possible deletion of a currently-used object. Accordingly, at (5), the manager 188 increments a reference count for the object, indicating that the object is being read by the VM instance 183. In one embodiment, a non-zero reference count for an object prevents deletion of the object from the local cache (excepting edge case scenarios, such as the entire cache being filled with objects having a non-zero reference count).
At (6), the local object manager 188 returns to the file system process 184 a location of the requested object in the worker 181 filesystem (e.g., the local cache). For example, where the local cache is a memory-mapped file, the manager 188 may return a bit-range within the file that corresponds to the object. Thereafter, at (7), the file system process 184 accesses the requested data from the indicated location, such as by reading from the indicated bit range. In one embodiment, the file system process 184 is configured to read only a subset of the object corresponding to the data requested to be read. For example, assume that each object is a 512 kilobytes in size, and that the objects in combination represent a disk image storing data in operating-system-level data blocks each of 4096 bytes (4 kilobytes). Each object might therefore contain 128 data blocks. Assuming that the instance 183 requested to read less than all data blocks of an object, the file system process 184 may identify the particular blocks within the object that have been requested, and access only those blocks from the worker filesystem. For example, the worker 181 may identify a particular bit range within the object represented the requested blocks, and read that bit range from the worker filesystem (which particular bit range falls within the broader bit range of the object as stored within the filesystem). Illustratively, if the particular bit range is the first 256 kb of an object, the worker 181 may read the first 256 kb of the bit range for the object as stored in the worker filesystem.
In some embodiments, objects may be stored in an encrypted manner, to preserve data confidentiality. To further this goal, the information required to decrypt each object may be stored in a manifest for a data set, and access to the manifest may be limited to the file system processes 184 that facilitate access to the data set. Accordingly, the local object manager 188 (among other components) may be restricted from reading the data of an object, and the data read by the file system process 184 at (7) may be in an encrypted form. At (8), the file system process 184 thus decrypts the data. In one embodiment, an encryption key for the object is stored within a manifest for the data set. Additional examples regarding storage of the encryption key in a manifest are provided in the ′369 Application, incorporated by reference above. Thus, the file system process 184 may retrieve the key for the object from the manifest and decrypt the read data using the key. In one embodiment, the object is encrypted using a block cipher, which can facilitate selective decryption of data from the object as opposed to requiring decryption of the entire object. For example, the file system process 184 may identify particular blocks encrypted using a block cipher, and decrypt those blocks using the encryption key. In the instance that objects are not encrypted, interaction (8) may be omitted.
At (9), the file system process 184 returns the requested data to the VM instance 183. Notably, the VM instance 183's views of the interactions noted above are limited to requesting to read data and being provided with that data, and are thus analogous to those interactions that would occur if the entire data set were stored locally within a user space 182 of the instance 183. These interactions may therefore be implemented without modifying configuration of the VM instance 183, and as such the serverless code execution system 110 may support use of snapshots and disk images generated under existing specifications and not specifically configured for the system 110. However, due to the on-demand retrieval of read data from such snapshots or disk images, the latency associated with use of such snapshots or images at the system 110 is substantially reduced relative to complete transfer of the snapshot or disk image to the worker 181 hosting the instance 183 prior to that use. Moreover, the computing resources used at the worker 181 are reduced, and the efficiency of such use is increased, by enabling multiple instances 183 to share access to individual objects and by loading only those objects that are actually used by an instance 183.
After accessing the data, the VM instance 183 processes the data at (10). Processing of data by a VM instance 183 is outside the scope of the present disclosure, and may correspond to any operations of the instance 183, the scope of which are commensurate with the wide variety of computing processes known in the art.
On completion of processing, the instance 183 illustratively notifies the file system process 184 at (11) that data access has completed. The notification may be generated, for example, by closing a handle to a file of the FUSE filesystem—a typical operation of code after completing use of data. To facilitate garbage collection, the file system process 184 generates a corresponding notification to the local object manager 188 at (12), indicating that the instance 183 has stopped access data of the object. The local object manager 188 thereafter at (13) decrements a reference counter for the object. Assuming that the reference counter has reached zero (indicating that no VM instance 183 is currently reading from the object), the local object manager 188 can then optionally conduct garbage collection with respect to the local cache, such as by deleting the object. Note that garbage collection is an optional process, and the local object manager 188 may in some or many cases maintain objects with zero reference counters. For example, the manager 188 may maintain frequently accessed objects even if such objects are not currently being accessed. The manager 188 may implement a variety of known cache eviction techniques to determine which objects to delete during garbage collection, including but not limited to a “least recently used” (or “LRU”) or “least frequently used” eviction policy.
As discussed above, the local object manager 188 is illustratively configured to facilitate retrieval of a data object when access to that object is requested by a file system process 184 associated with a VM instance 183. Illustrative interactions for facilitating such retrieval are shown in
The interactions of
At (2), the local object manager 188 inspects its local cache to determine whether the requested object exists within the cache. For example, the local object manager 188 may maintain a list of currently-cached objects, and may thus inspect the list to determine whether the requested object is cached. If so, the local object manager 188 can service the request for the object from the local cache, and no further interactions are required.
For the purposes of illustration, it is assumed in
After determining L2 cache locations, the local object manager 188, at (4), requests object parts from the determined L2 cache locations. The distributed L2 cache devices 170, in turn, return the requested object parts if stored in the L2 cache locations. Should the object not be stored, an L2 cache device 170 may return an “object not found” indicator. In one embodiment, the L2 cache devices 170 do not themselves implement “cache miss” handling, and do not attempt to retrieve a requested part from another location. Thus, implementation of the L2 cache device 170 is simplified relative to other caching techniques.
In the case that a threshold number of parts is retrieved from the L2 cache devices 170 (the threshold representing a minimum number of parts needed to recreate the object from erasure coded parts), the local object manager 188 can be configured to recreate the object from the parts and return the object to the requesting process. However, for the purposes of illustration, it is assumed in
On detecting that insufficient parts are stored in L2 cache devices 170, the local object manager 188, at (7), requests the object from the object storage system 190. As noted above, an initial request for an object may include designation of an object root 194 of the object storage system 190 that contains the object. The root may be identified, for example, as a logical directory of the system 190 containing the object. Thus, the manager 188 illustratively requests the object from the identified root 194. The system 190, in response, returns the object at interaction (8).
On obtaining the object, the local object manager 188 services the request for the object, such as by loading the object into a local cache and returning a location of the object in that cache to a requesting device (as discussed, e.g., above with reference to
To facilitate subsequent requests for the object, the local object manager 188 is further illustratively configured to store the object within the L2 cache as a set of erasure-coded parts. Thus, at (10), the local object manager 188 conducts erasure coding against the object to generate those parts that were detected to be missing from the L2 cache at interaction (6). For example, the object manager 188 may generate all parts for the object (if no parts were received). In some instances, where some but not all parts are received, the manager 188 may generate only those parts not received from L2 cache devices 170. At interaction (11), the manager 188 stores the parts in the appropriate L2 cache devices 170, which may be identified according to the location techniques noted above. Thus, subsequent requests for the object at the local object manager 188, or at other managers 188 associated with the L2 cache, may be serviced from the L2 cache without requiring retrieval from the object storage system 190.
While
As an illustration, consider an erasure coding that generates 5 parts from an object, and can tolerate a failure of 1 part. Further assume the parts are of different types, for example with 4 parts holding the actual data of the object and 1 part representing parity values for the 4 parts. One technique for using such parts would be to request the 4 parts holding the actual data of the object. Should any 1 part not be retrieved, an additional request for the parity data (the 5th part) could be made, and used to reconstruct the object from the 3 data-holding parts and the parity data part. This technique significantly increases latency, as the first four requests must complete (or time out), and then a fifth request (for the parity data) must be made. Thus, this technique provides resiliency but does not improve long tail latencies.
In accordance with embodiments of the present disclosure, the manager 188 may address this problem by simultaneously requesting all parts of the object, without regard to potential types. The manager 188 may further be configured to begin constructing the object as soon as a threshold number of parts are retrieved, without respect to whether outstanding requests for remaining parts exist. If it is assumed that response times of L2 cache devices 170 fall into a distribution, the latency of operation of the manager 188 can thus be reduced by effectively ignoring the latency of the last m devices 170, where m is the loss tolerance of the implemented erasure coding scheme (e.g., the specific mechanism of erasure coding being implemented, a variety of which are known in the art). In this manner, potential “long tail” scenarios (in which the distribution has a minority of requests that take excessively long times) are mitigated. Accordingly, even if some L2 devices 170 experience partial or total failures (up to the loss tolerance of the implemented erasure coding scheme), these failures would be expected not to increase the latency of operation of the manager 188 in obtaining the object. Thus, use of erasure coding as described herein can provide significant benefits relative to directly caching objects.
As discussed above, the number of objects stored on the system 110 to facilitate embodiments described herein may be large. Moreover, constant storage of these objects may not be strictly necessary to support operation of the serverless code execution system 110. For example, snapshots of VM instances 183 may facilitate rapid provisioning, but provisioning may nevertheless occur without such snapshots. Similarly, while storing a disk image as a set of portions may provide the benefits noted herein, the system 110 may additionally store the disk image as a single object on the object storage system 190, and thus it may be possible for the system 110 to recreate such portions at a later time if the portions are deleted. To balance use of computing resources to store portions against the improved performance realized by storing portions, the object storage system 190 may therefore be configured to store portions for recently used data sets while not storing portions for not-recently-used data sets.
To facilitate this operation, the object storage system 190 in one embodiment implements life cycled object roots 194, in which garbage collection occurs at a root 194 level, rather than attempting to remove individual portions or data sets. Each root 194 may exist within a particular life cycle state, and objects within the root 194 may be removed during a final life cycle state. In this way, the system 190 may avoid a need to maintain state information as to, for example, the last time at which an individual data set (or portion thereof) was used.
An example visualization of root 194 life cycle stages is shown in
New (Creating): This stage indicates that the system 190 is in the process of creating a root 194, such as by provisioning storage space on the system 190 to store data objects, populating the root 194 with any initial data objects, and the like. In this stage, the root 194 is not yet available for reading from or writing to.
The stages noted above are provided for illustrative purposes, and the life cycle of a root 194 may vary from these stages. For example, the “expired” stage may be omitted in some embodiments, such as those in which certainty can be achieved that the root leaves the retired state only after all operations regarding data in the root complete. In one embodiment, a single root 194 is maintained in the active stage at any time. For example, a new active root 194 may be created when transitioning of a current active root 194 to a retired stage. In other embodiments, multiple roots 194 are maintained in the active stage, and objects are divided among the roots 194 according to any of a number of load balancing techniques. For example, a first root 194 may store objects with a first range of identifiers, and a second root 194 may store objects with a second range of identifiers.
In general, objects may migrate between retired and active roots 194 in the manner shown in
At interaction (2), the root manager 188 then migrates in-use data sets that exist within a non-active root 194, if any. For example, the root manager 188 may iterate through the use notifications to determine a life cycle state of the root 194 associated with each notification. If the root 194 is in an active state, the root manager 188 may take no action. If the root is in an inactive state, such as retired or expired (which in some instances may be considered “sub-states” of a larger inactive state), the root manager 188 may copy the manifest and objects associated with the data set to an active root 194. Furthermore, the root manager 188 may modify the manifest such that the manifest indicates the active root 194 as a location for the objects, rather than the prior root 194. As noted above, in some embodiments objects are shared between data sets. As such, it is possible that a subset of the objects of a data set being copied already exist within the active root 194 (as part of an already-migrated data set, for example). The root manager 188 may therefore copy only objects not already present within the active root 194. Subsequent uses of the data set can thereafter be redirected to the active root 194, thus migrating reads away from non-active roots 194.
Notably, the interactions of
With reference to
The routine 800 begins at block 802, where the manager 188 receives a request for an object. The request may be generated, for example, by a file system process 184 associated with a VM instance 183 hosting execution of a task, such as by the instance 183 issuing a “read” of a data block within a data set. As noted above, the file system process 184 may provide the data set as a virtualized storage device (e.g., a mounted drive), and may thus translate (e.g., using a manifest for the data set) requests to read from the storage device into a request for an object containing the requested data. The request illustratively includes a an identifier of the object, such as a hash value of the object, MAC of the object, or other unique identifier, and a location of the object, such as within a directory within a root 194 containing the object.
At block 804, the manager 188 determines whether the object exists in a shared local cache. As discussed above, the shared local cache represents memory available to the manager 188 and a reading process, such as the file system process 184. For example, the shared local cache may be a memory-mapped file on a storage device of the worker 181, which file is accessible to the process 184. The manager 188 illustratively maintains a listing of objects within the local cache to facilitate implementation of block 804. As discussed above, the local cache may be shared among all processes 184 on the worker 181, and objects may be shared among different data sets associated with different tasks. As such, the local cache may have previously been populated with the requested object, such as by implementation of the routine 800 with respect to the currently requesting file system process 184 or another process 184 associated with another task execution. In some instances, the local cache may be pre-populated with objects independent of requests from processes 184, such as by pre-populating the cache with objects shared among a large number of data sets of commonly executed tasks. Examples of such objects include, for example, objects representing data of a commonly used operating system, library, utility, etc.
In the instance that the cache contains the requested object, the routine 800 proceeds to block 808 as discussed below. In the instance that the object does not exist within the cache, the routine 800 proceeds to block 806, where the manager 188 retrieves the object and stores it in the local cache. Retrieval of the object may include, for example, retrieval of the object from the root 194 location included within the initial request. In some instances, retrieval of the object may include retrieval from a second level cache, such as via the routine 900 discussed with reference to
After the object exists within the local cache, the manager 188 at block 808 provides to the requesting process 184 a pointer to a location, within the local cache, that includes the object. For example, where the cache is a memory mapped file, the manager 188 may return a memory pointer, within the file, that corresponds to a start of the requested object, as well as a length of the object within the file. The process 184 can therefore access the file at the location of the memory pointer, and read the file to access data of the object. As discussed above, the process 184 in some instances is configured for security purposes not to read the entire object, even though such object is accessible, but rather to read only a portion of the object requested by its respective requesting process (e.g., VM instance 183). The process 184 may additionally decrypt the read portion of the object, if such object is handled by the manager 188 in an encrypted form (again for security purposes).
In addition, at block 808 the manager 188 increments a reference counter for the object. The reference counter is illustratively used by the manager 188 to ensure that an object is not removed from the local cache while still being accessed by the process 184. Because the routine 800 may be implemented for each request of an object (with multiple instances of the routine 800 potentially implemented concurrently), and because objects may be shared among different processes 184, it is possible at block 808 that the reference counter for the object is non-zero, indicating that another process 184 also is currently accessing the file. Thus, using a reference counter (as opposed for example to a binary “referenced” or “non-referenced” status) can assist in tracking the number of processes 184 accessing an object.
At block 810, the manager 188 obtains a notification that access to the object is complete. The notification may be generated, for example, by a “close file” operation of a VM instance 183, indicating for example that the instance 183 has read the requested data and no longer requires access to that data. In another embodiment, the notification may correspond to a closing of a connection to the process 184 that requested the file, which may indicate for example a crash of the process 184 or other non-graceful shutdown. The manager 188, in response, decrements the reference counter for the object.
At block 812, the manager 188 determines whether the reference counter for the object has reached zero, indicating that no processes 184 are accessing the file. If so, the routine 800 proceeds to block 814, where the object is marked for garbage collection. The object can thereafter be deleted from the local cache, freeing up computing resources for other objects. In some instances, deletion itself occurs at block 814. In other instances, deletion occurs based on other factors, such as detection that free space in the local cache reaches a minimum threshold. While routine 800 shows this marking as a distinct step, in some cases a garbage collection process may use the reference counters of each object directly. For example, when a garbage collection process runs, such as in response to detecting a threshold minimum of free space available, the process may delete those objects with zero reference counters.
Thus, shared access to an object is provided on a worker 181 in a manner that facilitates rapid access to the data of that object while enabling efficient use of storage on the worker 181. The routine 800 then ends at block 816.
As discussed above, in some instances the system 110 may include a level two (“L2”) cache implemented by a distributed set of L2 cache devices 170. The L2 cache may illustratively store objects used by workers 181 within the fleet 180, making such objects accessible to the workers 181 in a manner that is more readily accessible than objects stored in object roots 192. For example, the L2 cache devices 170 may be closer to the workers 181 in terms of network distance, have a connection to workers 181 with more available bandwidth, have additional computing resources available to service requests from workers 181, have more computing resources dedicated to servicing these requests, or the like.
To facilitate rapid retrieval, objects may be stored in the L2 cache as a set of erasure-coded parts, such that only a less than all parts of an object are required to regenerate the object from the parts. Storage of erasure coded parts may, for example, reduce the “long tail” delays that may exist when an individual L2 cache experiences partial or complete failure, and therefore fails to return data or returns such data very slowly relative to a properly functioning device 170.
Unlike some traditional caching mechanisms, objects within the L2 cache may be managed by the consumers of the objects—the workers 181 themselves—rather than by a separate device facilitating interaction with the L2 cache. Thus, individual cache devices 170 may be configured relatively simply, to obtain requests for data stored in a store of the device 170 (e.g., part store 172) and to provide such data if it exists within the store or, if not stored in the store, to return an indication that such data does not exist.
To manage data in the L2 cache, each worker 181 may implement a cache management routine, an example of which is shown in
The routine 900 of
In one embodiment, the set of L2 cache locations is determined according to a load balancing algorithm as applied to the object to be retrieved. For example, manager 188 may utilize a consistent hash algorithm to load balance parts among services 170. Illustratively, the L2 cache devices 170 may be logically arranged within a ring, such that each device is associated with a location on the ring. The manager 188 may illustratively determine or calculate a hash value of the object or an identifier of the object (or, where objects are identified by hash values, use that hash value directly) and identity a location on the ring for the hash value. The manager 188 may then determine a “next” device 170 on the ring, and associate that device 170 with a first part of the object, such that the object is stored (and expected to be stored) at that device 170. Subsequent parts may be stored on subsequent devices 170 within the ring, e.g., such that parts 2 through 5 are stored at the 2nd through 5th devices 170 on the ring, as measured (in a given direction) from the location of the object's hash value. While consistent hashing is provided as an example, any number of load balancing techniques are possible. Each manager 188 can implement the same load balancing technique, such that the locations for parts of an object are deterministic and consistent across managers 188 without requiring coordination among managers 188 (e.g., to communicate regarding storage locations of parts).
On determining locations for parts, the manager 188, at block 904, requests the parts from each cache location (e.g., each L2 device 170 expected to store a corresponding part). The request may be, for example, an HTTP “GET” request for the object, as identified by the object's identifier.
Thereafter the routine 900 varies according to the determination at block 904 if whether sufficient parts are received at the manager 188. In
In some embodiments, the requests transmitted at block 904 occur simultaneously, with the local object manager 188 transmitting requests for all parts of the object to the identified cache devices 170. This approach can prioritize latency over bandwidth, as it might be expected to result in responses from each cache device 170 with minimal delay, and thus minimize time required to make a determination at block 906. In another embodiment, the manager 188 may transmit requests for only some parts at a first point in time, and transmit requests for a remainder of the parts at a later point in time. For example, it might be expected that a normal response time from the device 170 is a relatively short time period (e.g., ones to tens of milliseconds), while a delayed response time (such as due to congestion, device failure, etc.) is a relatively long time period (e.g., hundreds of milliseconds). The manager 188 may therefore, at a first point in time, transmit requests for only some parts of the object, such as a minimum number sufficient to generate the object. If one or more responses is not received within the expected normal response time window (e.g., 10 milliseconds, where normal responses are expected in under 10 milliseconds), the manager 188 may transmit requests for the remaining parts. By requesting less than all parts initially, bandwidth is conserved where each initial request is responded to within the initial response window. Moreover, by requesting the remaining parts after the normal response window, the total time required to make a determination at block 906 is still reduced relative to other caching techniques, such as storage of an object in a single device 170. For example, assume that one of the devices 170 storing an initially requested object does not respond in the normal response time window (e.g., under 10 ms), and that the manager 188 thus requests remaining parts after that window has passed. Assuming that a sufficient number of devices 170 respond to the requests for remaining parts within the normal time window, the manager 188 may nevertheless make a determination at block 906 in a period of around two times the normal response time window (e.g., around 20 milliseconds). Thus, so long as the expected time window for delayed responses is greater than 2 times the time window for normal responses, this approach of bifurcating requests into two time periods can provide reduced bandwidth usage while still providing for reduced latency relative to a single request for an object stored at a single device 170.
After the determination at block 906, if sufficient parts are retrieved, the routine 900 proceeds to block 908, where the manager 188 generates the object from the erasure coded parts. Specifics for generation of a data item from a set of erasure coded parts varies according to the particular erasure coding technique used, a variety of which are known in the art. Thus, details of such generation are not described in detail herein.
If sufficient parts are not retrieved, the routine 900 proceeds to block 910, where the manager 188 retrieves the object from an origin location. For example, the manager 188 may retrieve the object from a root 194 storing the object. In one embodiment, the request for the object identifies the origin location. In another embodiment, the manager 188 identifies the origin location, such as via interaction with root manager 188 to determine a root 194 in which the object is stored.
At block 912, after generating or retrieving the object, the manager 188 stores the object in the local cache. The object can thus be made available to a requesting device, such as via the routine 800 of
While block 912 can satisfy requirements that an object is retrieved (e.g., such that other processes awaiting the object, like the routine 800 of
These additional blocks begin at block 914, where the manager determines whether any requested parts (e.g., as requested at block 904) were not received. Notably, block 914 may evaluate as true even when sufficient blocks were received to regenerate the requested object (e.g., block 906 evaluated as true). This is because the L2 cache can benefit from store all parts of an object, not just a minimum number of parts, both for resiliency purposes and for purposes of speeding later retrieval. For example, it is possible that a non-retrieved part is stored at an L2 device 170 that operates more quickly than the L2 devices 170 that provided retrieved parts, thus speeding later implementations of block 906. Block 914 may take into account, for example, parts that were received after block 906 evaluated as true. For example, block 914 may evaluate as false if all parts were retrieved, even if block 906 evaluated as true based on retrieval of less than all parts.
If all parts were retrieved, there may be no need for the manager 188 to regenerate and store parts, and the routine 900 ends at block 918. However, if some parts were not retrieved, the routine 900 proceeds to block 916, where the manager 188 erasure codes the object into at least the unretrieved parts. Specifics for erasure coding a data item into a set of erasure coded parts varies according to the particular erasure coding technique used, a variety of which are known in the art. Thus, details of such erasure coding are not described in detail herein. Thereafter, at block 918, the manager 188 stores the unretrieved parts in their respective locations of the L2 cache (e.g., the devices 170 identified at block 902, from which the parts were not retrieved). Notably, blocks 916 and 918 may account for both situations in which one or more parts of the object were lost among the L2 cache (e.g., due to failure of a device 170) and in which the L2 cache simply lacked a given object (e.g., due to the object not being recently requested). Thus, the previously unretrieved parts are stored within the L2 cache and made available for subsequent implementations of the routine 900. The routine 900 then ends at block 918.
With reference to
The routine 1000 begins at block 1002, where the root manager 192 creates a new root on the system 190. The root illustratively represents a logical storage location on the system 190, such as a prefix within a hierarchy of storage locations. Creation of the root may include creation of the storage location, and in some instances may further include placement of pre-defined data within the storage location, such as commonly used objects (e.g., commonly referred to portions of data sets).
After creation of a root, the routine 1000 proceeds to block 1004, where the manager 192 sets the root's state to “active.” An active state indicates that the root is available for writing to by other components. For example, a frontend 120 may place a data set in the root, such as by dividing the data set into a number of portions and storing the portions as individual objects within the root, along with a manifest identifying the portions. As discussed above, an active root may also be available for reading from on the system 190. During the active stage, the manager 192 may illustratively respond to inquiries regarding active roots by providing an identifier of the active root.
The routine 1000 then proceeds to block 1006, where the manager 192 determines whether an active duration of the root has passed. The active duration may be set by an administrator of the system 190, such as based on a statistical measure for duration of use of data sets on the system 190. For example, the active duration may be established on the order of hours, days, weeks, etc. In one embodiment, the active duration is between 7 and 14 days. If the active duration has not yet passed, the routine 1000 continues to loop until the duration is reached.
Once the active duration has passed, the routine 1000 proceeds to block 1008, where the manager 192 transitions the root to a retired state. During the retired stage, the manager 192 is illustratively configured to stop identifying the root in response to inquiries for active roots, thus halting writing of new data to the root. However, the root may still be available for reading on the system 190.
In addition, during the retired state, the manager 192 may obtain notifications that a data set within the retired root is in fact actively used, as shown at block 1010. Such notifications may include, for example, provisioning a new execution environment with access to the data set, a device reading from the data set, or the like. If a notification is received at block 1010, the routine 1000 proceeds to block 1012, where the manager 192 copies the data set to an active root. In one embodiment, the manager 192 is configured to create a new active root prior to transitioning a currently-active root to a retired state. For example, the routine 1000 may be modified to include, prior to block 1008, a block that initiates an additional implementation of the routine 1000. Copying of the data set may include duplicating the data set in the location corresponding to the new root. In some embodiments, copying of the data set may include deleting the data set from the current root. However, in other embodiments, the data set is maintained in the current root to continue supporting reads of the data set from the current route. As noted above, in some instances data sets may be stored as a set of objects and a corresponding manifest, with at least some objects potentially being shared by other data sets. In these instances, copying of the data set may include referencing the manifest to identify the set of objects to be copied to the new root, and copying those objects within the set that do not already exist within the new root. After copying, the routine 1000 returns to block 1010.
After each active data set is copied to a new active root, the routine 1000 proceeds to block 1014, where the manager 192 determines whether a retired duration for the root has passed. If not, the routine returns to block 1010 until that duration has passed. The retired duration can generally be set according to similar considerations as the active duration. However, the retired duration may differ from the active duration (e.g., longer or shorter than the active duration). In some instances, a single retired duration is used, as measured from the last time at which a data set was indicated as active in the retired root. In other instances, multiple retired durations are used. For example, a first retired duration may be established from implementation of block 1008 (the transition to retired), and a second retired duration may be established from the last time at which a data set was indicated as active in the retired root. In one embodiment, the manager 192 requires all retired durations to have passed before block 1014 evaluates as true.
Thereafter, the manager 192 determines that no data is in active use on the root. Thus, at block 1016, the manager 192 deletes the root and the objects contained therein. Thus, garbage collection on the system 190 is accomplished and storage resources used to store data are reclaimed. The routine 1000 then ends at block 1018.
The routine 1000 is intended for illustration, and variations are possible and contemplated herein. For example, rather than deleting the root at block 1016, the manager 192 may instead mark the root as “garbage,” such that another element of the system 190 may later delete the root (e.g., as storage space is required). As another example, while the routine 1000 depicts a 4 stage lifecycle (new, active, retired, and dead), the routine 1000 may be modified to support additional stages, such as an “expired” stage. As discussed above, the expired stage may be used as a failsafe state, to prevent deletion of data still in use. In one embodiment, implementation of an expired state may include insertion of a new block between blocks 1014 and 1016, which inserts a delay in the root lifecycle corresponding to the expired state, with the delay timed to enable any pending operations on data of the root (e.g., copying of a data set to an active root) to complete. In another embodiment, this newly inserted block may be a decision block, that precludes transition to a dead state so long as any process (e.g., a copy process) is utilizing data of the root. In yet another embodiment, implementation of an expired state may be similar to that of the retired state, except that a notification of an active data set within an expired root may be reported as an error. Thus, to implement an expired stage, the routine 1000 may be modified to include another copy of blocks 1008-1014 in between blocks 1014 and 1016 as shown in
All of the methods and processes described above may be embodied in, and fully automated via, software code modules executed by one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods may alternatively be embodied in specialized computer hardware.
Conditional language such as, among others, “can,” “could,” “might” or “may,” unless specifically stated otherwise, are otherwise understood within the context as used in general to present that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Disjunctive language such as the phrase “at least one of X, Y or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y or Z, or any combination thereof (e.g., X, Y and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y or at least one of Z to each be present.
Unless otherwise explicitly stated, articles such as ‘a’ or ‘an’ should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.
Any routine descriptions, elements or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or elements in the routine. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, or executed out of order from that shown or discussed, including substantially synchronously or in reverse order, depending on the functionality involved as would be understood by those skilled in the art.
It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
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