Patent Publication Number: US-11392497-B1

Title: Low latency access to data sets using shared data set portions

Description:
BACKGROUND 
     Computing devices can utilize communication networks to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or to provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as a “data center,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public. 
     To facilitate increased utilization of data center resources, virtualization technologies allow a single physical computing device to host one or more instances of virtual machines that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical computing device can create, maintain, delete, or otherwise manage virtual machines in a dynamic manner. In turn, users can request computer resources from a data center, including single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources. 
     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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram depicting an illustrative environment in which a serverless code execution system can enable low latency execution of code by providing rapid access to data sets; 
         FIG. 2  depicts a general architecture of a computing device providing a worker of  FIG. 1 , which may host execution environments supporting execution of code and may provide rapid access to data sets relied on by such executions; 
         FIG. 3  is a flow diagram depicting illustrative interactions for handling a request to execute code on the serverless code execution system of  FIG. 1 , including providing an execution environment for the code with rapid access to a data set on which execution of the code relies; 
         FIG. 4  is a flow diagram depicting illustrative interactions for handling requests to read a data set by an execution environment that has been provisioned with rapid access to the data set; 
         FIG. 5  is a flow diagram depicting illustrative interactions for loading a portion of a data set into storage of worker of  FIG. 1  in response to a request to read the portion; 
         FIG. 6  is a visualization of a lifecycle for a data storage root, in which data sets may be stored to support execution of code on the serverless code execution system of  FIG. 1  while also enabling garbage collection on such data sets; 
         FIG. 7  is a flow diagram depicting illustrative interactions for managing root states according to the lifecycle shown in  FIG. 6 ; 
         FIG. 8  is a flow chart depicting an illustrative routine for management of objects on a worker of  FIG. 1 , to provide rapid access to data sets enabling executions on the worker; 
         FIG. 9  is a flow chart depicting an illustrative routine for implementing a level two cache of objects used by the workers of  FIG. 1 , including storage of objects in the level two cache as erasure-coded parts to facilitate rapid retrieval of such objects; and 
         FIG. 10  is a flow chart depicting an illustrative routine managing lifecycles of roots to enable garbage collection of data sets stored within those roots. 
     
    
    
     DETAILED DESCRIPTION 
     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 PYTHONT™ 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 “&#39;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 &#39;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 “&#39;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. 
       FIG. 1  is a block diagram of an illustrative operating environment  100  in which client devices  102  may interact with a serverless code executions system  110  via a network  104 . By way of illustration, various example client devices  102  are shown in communication with the serverless code execution system  110 , including a desktop computer, laptop, and a mobile phone. In general, the client devices  102  can be any computing device such as a desktop, laptop or tablet computer, personal computer, wearable computer, server, personal digital assistant (PDA), hybrid PDA/mobile phone, mobile phone, electronic book reader, set-top box, voice command device, camera, digital media player, and the like. The serverless code execution system  110  may provide the user computing devices  102  with one or more user interfaces, command-line interfaces (CLI), application programing interfaces (API), and/or other programmatic interfaces for generating and uploading user-executable source code (e.g., as part of a disk image or in association with a data set depended on by the code), invoking the user-provided source code (e.g., submitting a request to execute the source code on the on-demand code execution system  110 ), scheduling event-based code executions or timed code executions, tracking the user-provided source code, and/or viewing other logging or monitoring information related to their requests and/or source code. Although one or more embodiments may be described herein as using a user interface, it should be appreciated that such embodiments may, additionally or alternatively, use any CLIs, APIs, or other programmatic interfaces. 
     The illustrative environment  100  further includes one or more auxiliary services  106 , which can interact with the serverless code execution environment  110  to implement desired functionality on behalf of a user. Auxiliary services  106  can correspond to network-connected computing devices, such as servers, which generate data accessible to the serverless code execution environment  110  or otherwise communicate to the serverless code execution environment  110 . For example, the auxiliary services  106  can include web services (e.g., associated with the user computing devices  102 , with the serverless code execution system  110 , or with third parties), databases, really simple syndication (“RSS”) readers, social networking sites, or any other source of network-accessible service or data source. In some instances, auxiliary services  106  may be invoked by code execution on the serverless code execution system  110 , such as by API calls to the auxiliary services  106 . In some instances, auxiliary services  106  may be associated with the serverless code execution system  110 , e.g., to provide billing or logging services to the serverless code execution system  110 . In some instances, auxiliary services  106  actively transmit information, such as API calls or other task-triggering information, to the serverless code execution system  110 . In other instances, auxiliary services  106  may be passive, such that data is made available for access by the serverless code execution system  110 . For example, components of the serverless code execution system  110  may periodically poll such passive data sources, and trigger execution of code within the serverless code execution system  110  based on the data provided. While depicted in  FIG. 1  as distinct from the user computing devices  102  and the serverless code execution system  110 , in some embodiments, various auxiliary services  106  may be implemented by either the user computing devices  102  or the serverless code execution system  110 . 
     The 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  FIG. 1  as operating in a distributed computing environment including several computer systems that are interconnected using one or more computer networks (not shown in  FIG. 1 ). The serverless code execution system  110  could also operate within a computing environment having a fewer or greater number of devices than are illustrated in  FIG. 1 . Thus, the depiction of the serverless code execution system  110  in  FIG. 1  should be taken as illustrative and not limiting to the present disclosure. For example, the serverless code execution system  110  or various constituents thereof could implement various Web services components, hosted or “cloud” computing environments, and/or peer to peer network configurations to implement at least a portion of the processes described herein. 
     Further, the 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  FIG. 1 , the serverless code execution system  110  is illustrated as connected to the network  104 . In some embodiments, any of the components within the serverless code execution system  110  can communicate with other components of the serverless code execution system  110  via the network  104 . In other embodiments, not all components of the serverless code execution system  110  are capable of communicating with other components of the environment  100 . In one example, only the frontends  120  may be connected to the network  104 , and other components of the serverless code execution system  110  may communicate with other components of the environment  100  via the frontends  120 . 
     In  FIG. 1 , users, by way of user computing devices  102 , may interact with the serverless code execution system  110  to provide source code, and establish rules or logic defining when and how such code should be executed on the serverless code execution system  110 , thus establishing a “task.” For example, a user may wish to run a piece of code in connection with a web or mobile application that the user has developed. One way of running the code would be to acquire virtual machine instances from service providers who provide infrastructure as a service, configure the virtual machine instances to suit the user&#39;s needs, and use the configured virtual machine instances to run the code. In order to avoid the complexity of this process, the user may alternatively provide the code to the serverless code execution system  110 , and request that the on serverless code execution system  110  execute the code using one or more execution environments that are managed by the system  110 . The serverless code execution system  110  can handle the acquisition and configuration of compute capacity (e.g., containers, instances, etc., which are described in greater detail below) based on the code execution request, and execute the code using the compute capacity. The serverless code execution system  110  may automatically scale up and down based on the volume of request to execute code, thereby relieving the user from the burden of having to worry about over-utilization (e.g., acquiring too little computing resources and suffering performance issues) or under-utilization (e.g., acquiring more computing resources than necessary to run the code, and thus overpaying). 
     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  FIG. 1  may represent any of a number of object storage systems, such as AMAZON™&#39;s SIMPLE STORAGE SERVICE™ (or “S3™”). In accordance with embodiments of the present disclosure, a disk image may further be divided into a number of portions, each stored as a separate object on the object storage system  190 . These portions may be stored within an object root  194  on the storage system  190 , which represents a logical partition of the storage system  190 . As discussed in more detail below, objects within each root  194  may be used to facilitate low latency execution of code, and individual roots may be life-cycled and subject to garbage collection to facilitate removal of unused portions from the system. In one embodiment, submission of a disk image or other data set to the serverless code execution system  110  may cause the system  110  (e.g., via the frontends  120 ) to generate a set of portions representing the disk image or data set, and to store such portions in an “active” status object root  194 . Generation of portions from a data set is described in more detail in the &#39;369 Application, incorporated by reference above. In another embodiment, portions may be generated on an initial call to execute code. 
     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 &#39;593 Application, incorporated by reference above. Portions for such snapshots may be created, for example, according to the techniques of the &#39;369 Application when applied to such snapshots as an input data set. 
     In accordance with the teachings of the &#39;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 . 
     As shown in  FIG. 1 , the object storage system  190  further includes a root manager  192 , which is illustratively configured to manage life cycling of roots  194 , and to facilitate identification of the status of roots  194 . For example, the root manager  192  can provide interfaces enabling other elements of the system  110  to query for a set of roots  194  in a given life cycle state corresponding to a stage of the lifecycle (e.g., “active”), and provide a list of such roots  194  in return. Further, and as discussed in more detail below, the root manager  192  may facilitate transitioning of roots  194  between life cycle states, including copying of portions between different roots  194  based on indicators of use of such portions. 
     While not shown in  FIG. 1 , the object storage system  190  may include a variety of data stores other than object roots  194 , which may not be subject to, for example, the garbage collection techniques described herein. These other data stores may be used, for example, to store “original” data sets provided by end users, such that portions of disk images, snapshots, etc., may be recreated from original data sets even if such disk image or snapshot portions are subject to garbage collection. These other data stores may additionally be used, for example, to store metadata regarding a function. 
     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  106   w . 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  FIG. 1  are executed by execution environments hosted by a set of workers  181  within a worker fleet  180 . Each worker  181  is illustratively a host device configured to host multiple execution environments, which in  FIG. 1  are virtual machine instances  183 . Execution environments may alternatively include software containers, sometimes referred to as “OS-level virtualization,” another virtualization technology known in the art. Thus, where references are made herein to VM instances  183 , it should be understood that (unless indication is made to the contrary) a container may be substituted for such instances  183 . 
     While only a single instance  183  is shown in  FIG. 1 , each worker  181  may host a number of instances  183 . Each instance  183  may be isolated from other instances  183 , thus ensuring the security of code executions on the serverless code execution system  110 . For example, each instance  183  may be divided by a virtualization boundary, by virtue of the instance  183  being a virtual machine hosted by the worker  181 . In addition, each instance  183  may exist within a partitioned user space  182  on the worker  181 , which logically partitions resources of the worker  181  among instances  183 . Each user space  182  may, for example, represent a “chroot” jail—a known isolation technique for LINUX™ operating systems. 
     To facilitate rapid execution of code, each worker  181  may be configured to maintain a set of instances  183  in a “pre-warmed” state, being at least partially configured to begin execution of code. For example, instances may be created on the worker and configured with access to computing resources (CPU, RAM, drive storage, etc.). In 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  FIG. 1  also has access to a level two cache, provided by a set of distributed level two cache devices  170 . Each device  170  illustratively represents a server configured to store erasure-coded parts of objects used by the local object manager  188  (e.g., each object being a portion of a data set, such as a disk image or snapshot). Erasure-coded parts are stored within an object part store  172 , which may be any persistent or substantially persistent storage of the devices  170 . The level two cache devices  170  illustratively provided the local object managers  188  with higher bandwidth access to data that the object storage system  190 , such as by being located close to the worker fleet  180  in terms of network distance, having higher speed data storage or network connections, etc. As discussed above, rather than directly storing objects (data set portions), each device  170  may store erasure coded parts of objects, such that the object can be recreated with less than all such parts. As discussed in more detail below, storage of parts within the level two cache devices  170  may be controlled by the local object managers  188  of each worker  181 . For example, on retrieving an (uncached) object from the object storage system  190 , a local object manager  188  may erasure-encode the object into multiple parts, and then store those parts on a set of devices  170 . When another worker  181  desires to retrieve the object, the local object manager  188  of that worker  118  may retrieve the necessary parts of the object from those devices  170  and re-create the object from the parts, thus avoiding delay associated with retrieval of the object from the object storage system  190 . 
     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. 
       FIG. 2  depicts a general architecture of a computing system (a worker device  200 ) implementing the worker  181  of  FIG. 1 . The general architecture of the device  200  depicted in  FIG. 2  includes an arrangement of computer hardware and software that may be used to implement aspects of the present disclosure. The hardware may be implemented on physical electronic devices, as discussed in greater detail below. The device  200  may include many more (or fewer) elements than those shown in  FIG. 2 . It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. Additionally, the general architecture illustrated in  FIG. 2  may be used to implement one or more of the other components illustrated in  FIG. 2 . 
     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  FIG. 2  as a single set of memory  280 , memory  280  may in practice be divided into tiers, such as primary memory and secondary memory, which tiers may include (but are not limited to) RAM, 3D XPOINT memory, flash memory, magnetic storage, and the like. For example, primary memory may be assumed for the purposes of description to represent a main working memory of the device  200 , with a higher speed but lower total capacity than a secondary memory, tertiary memory, etc. 
     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  FIG. 2  is one illustrative configuration of such a device, of which others are possible. For example, while shown as a single device, a device  200  may in some embodiments be implemented as a logical device hosted by multiple physical host devices. In other embodiments, the device  200  may be implemented as one or more virtual devices executing on a physical computing device. While described in  FIG. 2  as a worker device  200 , similar components may be utilized in some embodiments to implement other devices shown in the environment  100  of  FIG. 2 , such as level two cache devices  170 , a root manager  192 , etc. 
     With reference to  FIG. 3 , illustrative interactions are depicted for handling a request to execute a task on the serverless code execution system  110 , including provisioning an environment with lazily-retrieved access to a data set to improve the latency for handling such a request. 
     The interactions of  FIG. 3  begin at ( 1 ), where a user device  102  submits a call to the frontend  120  to execute the task. Submission of a request may include transmission of specialized data to the frontend  120 , such as a HTTP packet or API call referencing the task. While the interactions of  FIG. 3  are described as including an explicit request to execute the task by the user device  102 , requests to execute the task may occur in a variety of manners, including submission of a call by auxiliary services  106  (not shown in  FIG. 3 ) or generation of a call by the serverless code execution system  110  (e.g., based on a rule to call the alias when specific criteria are met, such as elapsing of a period of time or detection of data on an auxiliary service  106 ). The request may include any information required to execute the task, such as parameters for execution, authentication information under which to execute the task or to be used during execution of the task, etc. 
     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  FIG. 3  depicts notification on selection of an environment, the placement service  160  may additionally or alternatively report data set use periodically. For example, the placement service  160  may be configured to maintain a system-wide view of environments leased among worker managers  140 , each of which is linked to a corresponding task, as well as data sets used to support execution of those tasks. Thus, the placement service  160  may periodically determine which data sets are associated with leased environments, and report use of those data sets to the object storage system  190 . 
     While  FIG. 3  depicts direct communication between the placement service  160  and the object storage system  190 , in some instances the system  110  may include additional elements that facilitate this communication. For example, the system  110  may include a task lifecycle management system (not shown in  FIG. 1 ) configured to maintain state information as to the tasks on the system  110 , which state information may include for example a status of the task as “accelerated” (e.g., having associated therewith a data set made rapidly available via embodiments of the present disclosure) or “non-accelerated” (e.g., by not having associated therewith such a data set, either by having no data set associated therewith or having a data set associated therewith that has not been made rapidly available via the present embodiments). Illustratively, a task may enter an “accelerated” state when the appropriate data set for the task has been divided into portions stored within a root of the object storage system  190 , such as on creation of the task, and may enter a non-accelerated state after a threshold period of time of non-use, corresponding to expected deletion of the portions from the object storage system  190 . Thus, the placement service  160  may transmit the notification at ( 7 ) to the task lifecycle management system, which may in turn transmit the notification to the object storage system  190 . In some instances, the task lifecycle management system may obtain notifications from the placement service  160  on creation of an execution environment for a task, while the task lifecycle management system may “batch” report notifications of use to the object storage system  190  on a periodic basis (e.g., every 6, 12, or 24 hours). 
     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  FIG. 3  are illustrative, and may be modified in some embodiments. For example, the placement service  160  may be responsible for initializing an execution environment on the worker  181 . Illustratively, on selection of an environment (e.g., at ( 5 )), the placement service  160  may remotely connect to the worker  181  to configure the environment, including providing to the environment a manifest for the data set, configuring the file system process  184  to provide local access to the data set, and the like. As another example, frontends  120  may in some configurations be configured to pass instructions to execute a task to workers  181 . Illustratively, a frontend  120  may, rather than distributing a call at ( 2 ), transmit a request to the worker manager  140  to identify an environment on a worker  181  to which the call should be distributed. After identifying that environment (e.g., via interactions ( 3 ) through ( 6 )), the worker manager  140  may return to the frontend  120  connection information for the environment, such as an IP address. The frontend  120  can then distribute the call directly to the environment, rather than passing the call through the manager  140 . Other modifications to the interactions of  FIG. 3  are possible. 
     With reference to  FIG. 4 , illustrative interactions will be described for reading from a data set by a VM instance  183 , in accordance with embodiments of the present disclosure. The interactions of  FIG. 4  may facilitate, for example, reading bytes from a disk image to support booting of an operating system or a runtime, execution of code, etc. The interactions of  FIG. 4  may similarly facilitate reading from a snapshot to facilitate restoration of a virtual machine state captured in that snapshot. 
     The interactions of  FIG. 4  begin at ( 1 ), where a VM instance  183  provided with access to a data set (e.g., via the interactions of  FIG. 3 ) requests to read data of the data set. For example, the VM instance  183  may attempt to read a set of blocks of the data set, identified by a particular location within the data set (e.g., logical block addresses within the disk image). In  FIG. 4 , the disk image is illustratively mounted as a Filesystem in User Space (FUSE) filesystem. Accordingly, the request is routed (e.g., by the operating system of the worker  181 ) to the file system process  184 , which illustratively represents a FUSE module provided the FUSE filesystem. 
     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  FIG. 4 . However, in brief, the local object manager  188  may maintain a local cache, such as a memory-mapped file, storing multiple objects that have been requested by various VM instances  183  executing on the worker  181 . Thus, if the local cache already includes the requested object, the manager  188  may simply use that object without re-loading the object into the cache. If the local cache does not include the object, the manager  188  retrieves the object from one of a variety of potential sources. 
     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 &#39;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 &#39;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  FIG. 5 . 
     The interactions of  FIG. 5  begin at ( 1 ), where the local object manager  188  gets a request for an object. The request may be made by a file system process  184  associated with a VM instance  183 , as discussed above with respect to  FIG. 4 . As noted above, the request may include an identifier of the object, as well as an object root  194  on the object storage system  190  that represents an “origin” for the object (e.g., a location from which to retrieve the object if a cached version of the object is not located). 
     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  FIG. 5  that the requested object is not locally cached. As such, the local object manager  188 , at ( 3 ), determines a set of locations within a level two (“L2”) cache storing parts of the object. As noted above, the system  110  may include a L2 cache implemented by a set of distributed L2 cache devices  170 . Moreover, rather than directly storing objects within individual devices  170 , each object in the L2 cache may be divided into multiple parts using erasure coding techniques, with the number of parts determined according to the particular erasure coding technique applied. Each part of an object may be stored on a different L2 cache device, with the device storing a particular part being determined according to one of a variety of load balancing techniques. In one embodiment, the L2 cache devices  170  are arranged within a consistent hash ring, and individual parts are distributed among the devices  170  according to a hashing algorithm. For example, a hash value of an object may be used to select an initial device  170 , and then parts making up the object may be stored on the initial device  170  and the subsequent n−1 devices of the ring. As another example, the hash value of each part may be calculated and used to select a device  170  in the ring with responsibility for storing the part. Each local object manager  188  may store information enabling determination of the devices  170  hosting a given part, such as by implementing the same algorithm at each manager  188 . Thus, storage of a part by a first manager  188  at a given device  170  would be expected to enable retrieval of that part by a second manager  188  from the L2 devices  170 . 
     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  FIG. 5  that the manager  188  has not obtained the threshold number of parts. As such, at ( 6 ), the manager  188  detects that an insufficient number of parts has been retrieved. Not that this may indicate any number of parts below the threshold, including zero parts. Interaction ( 6 ) may illustratively be based on positive indications from one or more devices  170  that the requested parts are not cached, a lack of indication from a device  170  within a threshold period, or a combination thereof. 
     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  FIG. 4 ). The initial request for the object is therefore satisfied. 
     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  FIG. 5  discusses a distributed L2 cache storing erasure-coded parts of objects, other configurations of L2 cache are possible. For example, an alternative L2 cache may use devices  170  that store entire objects, rather than erasure-coded parts. However, use of erasure-coded parts may provide a number of benefits. For example, erasure coding, as recognized in the art, enables resiliency in the case of failure by enabling an object to be re-created by less than all parts of the object. Moreover, in accordance with the present disclosure, erasure coding of parts can reduce “long tail” latencies for retrieval of objects by enabling creation of the object prior to retrieving all parts of the object, even if no outright failure has occurred. 
     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 5 th  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  FIG. 6 . As shown therein, each root  194  may transition through the following stages: New (Creating); Active; Retired (read only); Expired (no reads expected); and Dead (deleting). In one embodiment, the life cycle is one directional, and roots  194  are permitted to progress only in the direction shown in  FIG. 6 . The illustrative life cycle stages may be described as follows: 
     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.
         Active: This stage indicates that the system  190  has completed creation of the root  194 . New objects may be written to the root  194 , such as in connection with creation of a new task on the system  110  or division of a data set for a task into portions to facilitate rapid execution of a task. Objects may be read from the root  194 , such as to support execution of tasks. Roots  194  may be held in this stage for a predefined period of time selected as appropriate for garbage collection. For example, roots  194  may be held in the active stage for 1 day, 7 days, 14 days, 30 days, etc., before transitioning to a retired state.   Retired (read only): This stage indicates that the system  190  has initiated garbage collection on the root  194 , and is maintaining the root  194  to determine which (if any) objects in the root  194  are in use by environments of the system  110 . Because some objects may still be in use, the root  194  supports reading of objects. However, writing of new objects to the root  194  is disallowed. An indication of use of an object within a retired root  194 , such as provisioning of a new execution environment with a data set including the object, illustratively results in copying of the object to an active root  194 . Thereafter, subsequent environments provisioned with the data set can be supported by the copy in the active root  194 . Thus, reads from a retired root  194  are progressively reduced during this stage. Roots  194  may be held in this stage for a predefined period of time as appropriate for garbage collection (e.g., 1, 7, 14, or 30 days as noted above), before transitioning to an expired state. In one embodiment, reading from a retired root  194  prevents the root  194  from transitioning to an expired state for a subsequent period. Thus, transition to an expired state is expected to occur only when no objects are being read from the retired root  194 .   Expired (no reads expected): This stage indicates that the system  190  has determined that no objects within the root  194  are still in use by execution environments, and thus that the root  194  may be safely deleted. However, the system  190  may maintain the root  194  in an expired state as a failsafe, in case objects of the root  194  are still in use by other processes. For example, the system  190  may, during a retired lifecycle stage, respond to use notifications for an object by copying that object to a new active root. In some cases, such a copy operation may still be occurring when a root transitions from the retired state. Thus, the root can be held in an expired state to ensure that such copy operations completed. Use of an expired state can further provide certainty that all elements of the object storage system  190  have halted use of objects within the root, which may be difficult to confirm given the distributed nature of the object storage system  190 . In some embodiments, reading of an object from a root  194  in this stage may indicate an error on the system  190 , and may pause life cycle migration of the root  194  until the error is resolved. Should no reads from the expired root  194  occur, the root  194  then transitions to a dead stage after a predefined period of time as appropriate for garbage collection. In some instances, the predefined period may be set relative to entering the expired state (e.g., n hours after initially becoming expired). In other instances, the period may be set relative to a last detected operation relating to an object in the root (e.g., n hours after a last copy of an object from the root completes). In one embodiment, execution environments may be precluded from reading data from an expired root, to further prevent load on that root. As such, reads from expired roots may be limited, for example, to migration of data to an active root.   Dead (deleting): This stage indicates that the system  190  is in the process of deleting the root  194 , including all objects stored within the root  194 . Deletion of the root  194  thus constitutes garbage collection with respect to those objects. On completion, the root  194  is removed from the system  190 .       

     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  FIG. 6 . Migration of objects is illustratively controlled by a root manager  188 , with illustrative interactions for migrating objects being shown in  FIG. 7 . In the embodiment of  FIG. 7 , objects are copied between roots  194  at the level of individual data sets. As noted above, each data set may be associated with a manifest that indicates a set of objects making up the data set. In this example embodiment, the manifest for a data set is stored within a root  194  alongside the set of objects making up the data set. When a new execution environment is to be provisioned with a data set, a component of the system  110  (e.g., the placement service  160 ) may determine a youngest-stage root  194  containing the data set (e.g., the manifest and corresponding objects), and instruct a worker  181  to use the manifest in order to provision the environment with access to the data set. The placement service  160  may further notify the root manager  188  of use of the data set within the determined root  194 . In one embodiment, the placement service  160  submits such notifications on each leasing of an environment. In another embodiment, the placement service  160  submits such notifications periodically, listing all data sets associated with leased environments and their associated roots  194 . These notifications are shown in  FIG. 7  as received at the root manager  188  at interaction ( 1 ). 
     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  FIG. 7  may result in redundancy in storing objects, and may also result in seemingly unnecessary copying of data between roots  194 . That is, if an object is continuously used, the interactions of  FIG. 7  can result in the object being continuously copied between roots  194  as those roots  194  are life cycled. The benefits of this life cycling approach may therefore not be immediately apparent. However, while this copying does result in use of computing resources to copy objects between roots  194 , it also enables garbage collection to occur in a manner that overcomes significant hurdles of past techniques. For example, the approach described with respect to  FIGS. 6 and 7  removes a need to track a last-used time of individual objects (or even individual data sets). Rather, the use indicators obtained at the root manager  188  may be viewed as a “to do” list of the manager  188 , and these indicators may be discarded by the manager  188  after being processed in the manner described above. The amount of state information maintained by the system  190  is therefore substantially reduced. Moreover, because the garbage collection described herein is “coarse-grained” (e.g., occurring at a granularity of a root  194 , as opposed to a data set or individual object), the likelihood of error due to incorrect operation is substantially reduced. In this context, “incorrect” operation does not necessarily indicate failures of an individual process, but rather the difficulty of reference counting within a distributed system. Put in other terms, because of the distributed nature of the system  190 , it is difficult for any component to maintain perfect information as to the state of the system  190  with respect to an individual object or data set. Thus, fine-grained garbage collection may be particularly susceptible to incorrect operation. Moreover, the amount of data copied between roots  194  may be limited in practice due to the nature of operation of the serverless code execution system  110 . For example, end users may frequently modify their tasks, such that tasks on average have a usage life span of only a few weeks or days. By setting a life span of an active root  194  commensurate with this average usage life span (e.g., as a 7 day active root  194  life span when tasks have a 5-10 day usage life span), the proportion of data copied between roots  194  can be reduced to an acceptable level, particularly given the benefits of this approach with respect to reduction in errors. 
     With reference to  FIG. 8 , an illustrative routine  800  will be described for management of objects on a worker  181  to facilitate rapid access to a data set relied on to support execution of a task within an execution environment of the worker  181 . The routine  800  may be executed, for example, by the local object manager  188 . 
     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  FIG. 9 , below. 
     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  FIG. 9 . The routine  900  of  FIG. 9  may be implemented, for example, by a local object manager  188 . In one embodiment, the routine  900  may be used to retrieve objects not cached within a local cache of the object manager  188 , such as to fulfill block  806  of  FIG. 8 . The routine  900  thus assumes that the manager  188  has identified an object to retrieve, such as an object requested by a file system process  184  in connection with the routine  800  of  FIG. 8 . 
     The routine  900  of  FIG. 9  begins at block  902 , where the manager  188  determines a set of L2 cache locations based on the object. As discussed above, each object may be stored in the L2 cache as a set of erasure-coded parts, with the number of such parts determined according to the particular erasure coding implemented by the manager  188 . The present description will assume, for illustration only, that an object is divided into 5 parts, of which only 3 are needed to regenerate the object (a “loss tolerance” of 2 parts). Other numbers of parts and loss tolerances are possible. 
     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 2 nd  through 5 th  devices  170  on the ring, as measured (in a given direction) from the location of the object&#39;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&#39;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  FIG. 9 , “sufficient parts” refers to the minimum number of parts required to generate the object from erasure coded parts. For example, in the assumed erasure coding configuration, three parts would be sufficient. In one embodiment, block  904  is re-evaluated as each part is received from a device  170 , such that the block  904  evaluates as true directly after sufficient parts have been received, even if outstanding requests for other parts exist and regardless of whether such outstanding requests eventually result in gathering additional parts. As such, the routine  900  need not be delayed awaiting such excess parts. This lack of delay is particularly beneficial in instances where one or more devices  170  take significantly longer to respond to the requests than other devices  170 , and where those delayed devices  170  store parts not needed to generate the object. In some embodiments, evaluation at block  906  may similarly be “short circuited” when the manager  188  determines that it is not possible to retrieve sufficient parts. For example, where the loss tolerance of the used erasure coding is 2 parts and the manager  188  receives responses from 3 devices  170  that the relevant parts are not stored therein, the manager  188  may determine that block  906  has evaluated to false without delay while awaiting responses from other devices  170 . 
     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  FIG. 8 . 
     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  FIG. 8  need not be delayed), the routine  900  includes a number of further blocks related to management of the L2 cache. These additional blocks may illustratively be implemented in an asynchronous manner relative to requests for objects handled by the manager  188 . For example, the additional blocks may be delayed by the manager  188  until sufficient resources are available at the manager  188  to implement the blocks. 
     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 re-generate 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  FIG. 10 , an illustrative routine  1000  will be described for conducting garbage collection on an object storage system using a life-cycled root. The routine  1000  may be implemented, for example, by a root manager  192  of the object storage system  190  in order to remove unused data from the system  190  and thus reclaim computing resources. 
     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&#39;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  FIG. 10 , and to modify the second copy of these blocks such that, in the second copy, implementation of block  1012  raises an error message to an administrator of the system  190 . In still other embodiments, another copy of blocks  1008 - 1014  in between blocks  1014  and  1016  as shown in  FIG. 10 , and block  1012  may be modified to raise an error but not to cause copying of the data to an active root. Other modifications to  FIG. 10  are possible. 
     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.