Patterns of access and/or behavior can be analyzed and persisted for use in pre-fetching data from a physical storage device. In at least some embodiments, data can be aggregated across volumes, instances, users, applications, or other such entities, and that data can be analyzed to attempt to determine patterns for any of those entities. The patterns and/or analysis can be persisted such that the information is not lost in the event of a reboot or other such occurrence. Further, aspects such as load and availability across the network can be analyzed to determine where to send and/or store data that is pre-fetched from disk or other such storage in order to reduce latency while preventing bottlenecks or other such issues with resource availability.
As an increasing amount of information processing is being performed electronically, and as the speed of that processing increases, there is a corresponding demand to improve the performance of the systems and services that manage the information being processed. A substantial amount of data is still stored on magnetic disk drives and similar devices, due at least in part to the relative low cost of these devices, but the time needed to read data to, and write data from, these devices is a significant source of latency. One approach to reducing latency is to pre-fetch data from these devices and store the information in faster memory (e.g., solid state memory), but conventional computing systems utilizing locally attached disks typically must be conservative in issuing pre-read commands as a read command utilizes bus bandwidth, and there is a limited amount of solid state memory available to hold the pre-fetched data. Further, there are no existing approaches to accurately predict which data should be pre-fetched, which results in either a smaller amount of data being pre-fetched than is needed, which results in lesser latency improvements, or a significant amount of data being pre-fetched that is not needed, which can result in a significant drain on available network resources.
Even where conventional systems look to recent read requests to determine data to be pre-fetched, these systems are typically limited in the amount of data they can access for the determinations. For example, an operating system (OS) on a computer might look at certain processes, and/or a RAID controller might look at data at another level independent of the OS. A disk drive might also have some amount of cache and do some level of pre-fetching as well. At each level, a decision can be made based on read requests received in the recent past, which can be limited in scope. For example, the disk drive will have the least amount of information and the most narrow view, but will not affect any other devices as the drive is only occupying its own cache and spindle time. Pre-fetching reads at the RAID level also can reduce available RAID cache, and moving up to the OS level can provide a broader view but can end up also filling OS memory. Thus, as the amount of information increases the potential for negatively impacting performance of the network increases as well. Further, such information typically is not persisted such that each time a device is rebooted, a significant amount of time passes between reads, or any other such action occurs, the historical information is lost and the prediction has to start from scratch.
FIG. 6 illustrates an example environment that can take advantage of functionality of the various embodiments.
Systems and methods in accordance with various embodiments of the present disclosure may overcome one or more of the aforementioned and other deficiencies experienced in conventional approaches to managing input/output (I/O) operations in a computing environment. In particular, various embodiments can analyze information such as user behavior and/or read patterns in order to predict subsequent read requests (i.e., for sequential reads). Various approaches can read the information (e.g., blocks of data) such that the data is readily available upon a subsequent request for that data. Further, at least some embodiments can analyze current conditions of the computing system, network, or other such components to determine how to handle the data before that data is requested.
Usage patterns can be determined and utilized to predict behavior for a user, application, or other such entity in any appropriate environment. For example, usage patterns can be monitored in a system where virtualized operating system instances utilize attached network storage. The underlying operating system hosting the instances can have visibility into information such as disk read patterns issued by the virtual instance. By analyzing these patterns the underlying operating system can predict subsequent reads, and thus can issue speculative pre-read instructions to a storage server or other such storage device in order to reduce the read latency of a subsequent read operation issued by the virtualized operating system. Such an approach provides for relatively inexpensive (from a resource standpoint) speculative read operations that are based upon the behavior of the respective user, application, etc.
The use of speculative reads, as well as aspects such as when to use such reads and the extent of such usage, can be balanced against the potential waste in resources. Since resources such as disk drives are major sources of latency for read requests, the ability to pre-fetch data from disk and store that data in memory can reduce the overall latency of the system. On the other hand, incorrectly speculating on subsequent read requests can squander resources such as bus bandwidth, available memory, and spindle time.
Speculative read commands, however, can be relatively inexpensive as the commands can take the form of instructions to a storage server, or other such storage device, to read the data into local memory in preparation of sending that data across the network (e.g., the local bus). Approaches in accordance with various embodiments can also analyze the current load on the system, such as the workload on the storage server or traffic across the network, and can use such analysis to determine whether to pause a speculative read process. In one embodiment, a storage server might have a committed rate of I/O operations for at least a period of time, which prevents the storage server from performing speculative reads over that period. In systems with sufficient extra resource capacity, however, the tolerance for inaccurate speculations about data reads can be greater, such that the amount of speculative reads can be increased, even if the accuracy is not above a certain acceptance threshold, in order to decrease latency and provide other such performance payoffs.
In at least some embodiments, information about various read patterns and analysis can be stored such that the information is persistent across restarts or other such events. In conventional systems, the operating system looks at recent disk I/O patterns to make decisions. Any operations issued on a previous boot cycle will be lost because the data is stored in memory. In various embodiments, however, disk I/O patterns and other such information can be persisted by the hosting operating system (e.g., in the Dom-0). Such an approach enables the patterns to be persisted across restarts, such that an analyzing component can have data available at all relevant times, even after a restart, and thus can make more accurate predictions, which result in better read performance, etc.
In at least some embodiments, a system or service can have a view into more than just the operations issued by the virtualized operating system. Such a “broader” view can provide improved optimization, as an optimizer component can combine pattern data with data gathered from other optimizer components running on separate, but related, instances. Information that can be aggregated for such purposes can include time-of-day trends, data from all instances owned by the same customer, and data from all instances running a particular application (e.g., a relational database management system).
In at least one embodiment, the I/O operations for an application or client can be monitored to attempt to determine read patterns. For example, an application submitting a request for a first read bit might typically submit subsequent read requests for the next two adjacent read bits from a data storage device (e.g., a disk drive, data volume, or other block storage). If the client or application requests three adjacent bits with a sufficient frequency, rate, or other such threshold or criterion, an analysis module or other such component can determine to pre-fetch the next two bits whenever a read request is received for that client or application. Such an approach minimizes the latency for read operations by performing certain tasks ahead of time, thereby minimizing the time delay after a read request due to extra disk revolutions, seek operations, or other such actions.
The monitoring of usage patterns and pre-fetching of data based on speculative reads can be utilized with a number of different systems and services. In many embodiments, the speculation is driven by a storage node or storage entity in a data or network environment. For example, a storage entity such as a block data storage service can utilize multiple server storage systems to reliably store block data that may be accessed and used over one or more networks by any of various users, applications, processes, and/or services. Users of the block data storage service may each create one or more block data storage volumes that each have a specified amount of block data storage space, and may initiate use of such a block data storage volume (also referred to as a “volume” herein) by one or more executing programs, with at least some such volumes having copies stored by two or more of the multiple server storage systems so as to enhance volume reliability and availability to the executing programs. As one example, the multiple server block data storage systems that store block data may in some embodiments be organized into one or more pools or other groups that each have multiple physical server storage systems co-located at a geographical location, such as in each of one or more geographically distributed data centers, and the program(s) that use a volume stored on a server block data storage system in a data center may execute on one or more other physical computing systems at that data center.
In addition, in at least some embodiments, applications that access and use one or more such non-local block data storage volumes over one or more networks may each have an associated node manager that manages the access to those non-local volumes by the program, such as a node manager module that is provided by the block data storage service and/or that operates in conjunction with one or more Block Data Service (BDS) System Manager modules. For example, a first user who is a customer of the block data storage service may create a first block data storage volume, and execute one or more program copies on one or more computing nodes that are instructed to access and use the first volume (e.g., in a serial manner, in a simultaneous or other overlapping manner, etc.). When an application executing on a computing node initiates use of a non-local volume, the application may mount or otherwise be provided with a logical block data storage device that is local to the computing node and that represents the non-local volume, such as to allow the executing program to interact with the local logical block data storage device in the same manner as any other local hard drive or other physical block data storage device that is attached to the computing node (e.g., to perform read and write data access requests, to implement a file system or database or other higher-level data structure on the volume, etc.). For example, in at least some embodiments, a representative logical local block data storage device may be made available to an executing program via use of an appropriate technology, such as GNBD (“Global Network Block Device”) technology. In addition, when an application interacts with the representative local logical block data storage device, the associated node manager may manage those interactions by communicating over one or more networks with at least one of the server block data storage systems that stores a copy of the associated non-local volume (e.g., in a manner transparent to the executing program and/or computing node) so as to perform the interactions on that stored volume copy on behalf of the executing program. Furthermore, in at least some embodiments, at least some of the described techniques for managing access of applications and services to non-local block data storage volumes are automatically performed by embodiments of a node manager module.
In at least some embodiments, block data storage volumes (or portions of those volumes) may further be stored on one or more remote archival storage systems that are distinct from the server block data storage systems used to store volume copies. In various embodiments, the one or more remote archival storage systems may be provided by the block data storage service (e.g., at a location remote from a data center or other geographical location that has a pool of co-located server block data storage systems), or instead may be provided by a remote long-term storage service and used by the block data storage, and in at least some embodiments the archival storage system may store data in a format other than block data (e.g., may store one or more chunks or portions of a volume as distinct objects).
In some embodiments, at least some of the described techniques are performed on behalf of a program execution service that manages execution of multiple programs on behalf of multiple users of the program execution service. In some embodiments, the program execution service may have groups of multiple co-located physical host computing systems, and may execute users' programs on those physical host computing systems, such as under control of a program execution service (“PES”) system manager, as discussed in greater detail below. In such embodiments, users of the program execution service (e.g., customers of the program execution service who pay fees to use the program execution service) who are also users of the block data storage service may execute programs that access and use non-local block data storage volumes provided via the block data storage service. In other embodiments, a single organization may provide at least some of both program execution service capabilities and block data storage service capabilities (e.g., in an integrated manner, such as part of a single service), while in yet other embodiments the block data storage service may be provided in environments that do not include a program execution service (e.g., internally to a business or other organization to support operations of the organization).
In addition, the host computing systems on which programs execute may have various forms in various embodiments. Multiple such host computing systems may, for example, be co-located in a physical location (e.g., a data center), and may be managed by multiple node manager modules that are each associated with a subset of one or more of the host computing systems. At least some of the host computing systems may each include sufficient computing resources (e.g., volatile memory, CPU cycles or other CPU usage measure, network bandwidth, swap space, etc.) to execute multiple programs simultaneously, and, in at least some embodiments, some or all of the computing systems may each have one or more physically attached local block data storage devices (e.g., hard disks, tape drives, etc.) that can be used to store local copies of programs to be executed and/or data used by such programs. Furthermore, at least some of the host computing systems in some such embodiments may each host multiple virtual machine computing nodes that each may execute one or more programs on behalf of a distinct user, with each such host computing system having an executing hypervisor or other virtual machine monitor that manages the virtual machines for that host computing system. For host computing systems that execute multiple virtual machines, the associated node manager module for the host computing system may in some embodiments execute on at least one of multiple hosted virtual machines (e.g., as part of or in conjunction with the virtual machine monitor for the host computing system), while in other situations a node manager may execute on a physical computing system distinct from one or more other host computing systems being managed.
The server block data storage systems on which volumes are stored may also have various forms in various embodiments. In at least some embodiments, some or all of the server block data storage systems may be physical computing systems similar to the host computing systems that execute programs, and in some such embodiments may each execute server storage system software to assist in the provision and maintenance of volumes on those server storage systems. For example, in at least some embodiments, one or more of such server block data storage computing systems may execute at least part of the BDS System Manager, such as if one or more BDS System Manager modules are provided in a distributed peer-to-peer manner by multiple interacting server block data storage computing systems. In other embodiments, at least some of the server block data storage systems may be network storage devices that may lack some I/O components and/or other components of physical computing systems, such as if at least some of the provision and maintenance of volumes on those server storage systems is performed by other remote physical computing systems (e.g., by a BDS System Manager module executing on one or more other computing systems). In addition, in some embodiments, at least some server block data storage systems each maintains multiple local hard disks, and stripes at least some volumes across a portion of each of some or all of the local hard disks. Furthermore, various types of techniques for creating and using volumes may be used, including in some embodiments to use LVM (“Logical Volume Manager”) technology.
In at least some embodiments, some or all block data storage volumes each have copies stored on two or more distinct server block data storage systems, such as to enhance reliability and availability of the volumes. By doing so, failure of a single server block data storage system may not cause access of executing programs to a volume to be lost, as use of that volume by those executing programs may be switched to another available server block data storage system that has a copy of that volume. In such embodiments, consistency may be maintained between the multiple copies of a volume on the multiple server block data storage systems in various ways. For example, in some embodiments, one of the server block data storage systems is designated as storing the primary copy of the volume, and the other one or more server block data storage systems are designated as storing mirror copies of the volume in such embodiments, the server block data storage system that has the primary volume copy (referred to as the “primary server block data storage system” for the volume) may receive and handle data access requests for the volume, and in some such embodiments may further take action to maintain the consistency of the other mirror volume copies (e.g., by sending update messages to the other server block data storage systems that provide the mirror volume copies when data in the primary volume copy is modified, such as in a master-slave computing relationship manner). Various types of volume consistency techniques may be used, with additional details included below.
In addition to maintaining reliable and available access of executing programs to block data storage volumes by moving or otherwise replicating volume copies when server block data storage systems become unavailable, the block data storage service may perform other actions in other situations to maintain access of executing programs to block data storage volumes. For example, if a first executing program unexpectedly becomes unavailable, in some embodiments the block data storage service and/or program execution service may take actions to have a different second executing program (e.g., a second copy of the same program that is executing on a different host computing system) attach to some or all block data storage volumes that were in use by the unavailable first program, so that the second program can quickly take over at least some operations of the unavailable first program. The second program may in some situations be a new program whose execution is initiated by the unavailability of the existing first program, while in other situations the second program may already be executing (e.g., if multiple program copies are concurrently executed to share an overall load of work, such as multiple Web server programs that receive different incoming client requests as mediated by a load balancer, with one of the multiple program copies being selected to be the second program; if the second program is a standby copy of the program that is executing to allow a “hot” swap from the existing first program in the event of unavailability, such as without the standby program copy being actively used until the unavailability of the existing first program occurs; etc.). In addition, in some embodiments, a second program to which an existing volume's attachment and ongoing use is switched may be on another host physical computing system in the same geographical location (e.g., the same data center) as the first program, while in other embodiments the second program may be at a different geographical location (e.g., a different data center, such as in conjunction with a copy of the volume that was previously or concurrently moved to that other data center and will be used by that second program). Furthermore, in some embodiments, other related actions may be taken to further facilitate the switch to the second program, such as by redirecting some communications intended for the unavailable first program to the second program.
As previously noted, in at least some embodiments, some or all block data storage volumes each have copies stored on two or more distinct server block data storage systems at a single geographical location, such as within the same data center in which executing programs will access the volume by locating all of the volume copies and executing programs at the same data center or other geographical location, various desired data access characteristics may be maintained (e.g., based on one or more internal networks at that data center or other geographical location), such as latency and throughput. For example, in at least some embodiments, the described techniques may provide access to non-local block data storage that has access characteristics that are similar to or better than access characteristics of local physical block data storage devices, but with much greater reliability that is similar to or exceeds reliability characteristics of RAID (“Redundant Array of Independent (or Inexpensive) Disks”) systems and/or dedicated SANs (“Storage Area Networks”) and at much lower cost. In other embodiments, the primary and mirror copies for at least some volumes may instead be stored in other manners, such as at different geographical locations (e.g., different data centers), such as to further maintain availability of a volume even if an entire data center becomes unavailable. In embodiments in which volume copies may be stored at different geographical locations, a user may in some situations request that a particular program be executed proximate to a particular volume (e.g., at the same data center at which the primary volume copy is located), or that a particular volume be located proximate to a particular executing program, such as to provide relatively high network bandwidth and low latency for communications between the executing program and primary volume copy.
Furthermore, access to some or all of the described techniques may in some embodiments be provided in a fee-based or other paid manner to at least some users. For example, users may pay one-time fees, periodic (e.g., monthly) fees and/or one or more types of usage-based fees to use the block data storage service to store and access volumes, to use the program execution service to execute programs, and/or to use archival storage systems (e.g., provided by a remote long-term storage service) to store long-term backups or other snapshot copies of volumes. Fees may be based on one or more factors and activities, such as indicated in the following non-exclusive list: based on the size of a volume, such as to create the volume (e.g., as a one-time fee), to have ongoing storage and/or use of the volume (e.g., a monthly fee), etc.; based on non-size characteristics of a volume, such as a number of mirror copies, characteristics of server block data storage systems (e.g., data access rates, storage sizes, etc.) on which the primary and/or mirror volume copies are stored, and/or a manner in which the volume is created (e.g., a new volume that is empty, a new volume that is a copy of an existing volume, a new volume that is a copy of a snapshot volume copy, etc.); based on the size of a snapshot volume copy, such as to create the snapshot volume copy (e.g., as a one-time fee) and/or have ongoing storage of the volume (e.g., a monthly fee); based on the non-size characteristics of one or more snapshot volume copies, such as a number of snapshots of a single volume, whether a snapshot copy is incremental with respect to one or more prior snapshot copies, etc.; based on usage of a volume, such as the amount of data transferred to and/or from a volume (e.g., to reflect an amount of network bandwidth used), a number of data access requests sent to a volume, a number of executing programs that attach to and use a volume (whether sequentially or concurrently), etc.; based on the amount of data transferred to and/or from a snapshot, such as in a manner similar to that for volumes; etc. In addition, the provided access may have various forms in various embodiments, such as a onetime purchase fee, an ongoing rental fee, and/or based on another ongoing subscription basis. Furthermore, in at least some embodiments and situations, a first group of one or more users may provide data to other users on a fee-based basis, such as to charge the other users for receiving access to current volumes and/or historical snapshot volume copies created by one or more users of the first group (e.g., by allowing them to make new volumes that are copies of volumes and/or of snapshot volume copies; by allowing them to use one or more created volumes; etc.), whether as a one-time purchase fee, an ongoing rental fee, or on another ongoing subscription basis.
In some embodiments, one or more application programming interfaces (APIs) may be provided by the block data storage service, program execution service and/or remote long-term storage service, such as to allow other programs to programmatically initiate various types of operations to be performed (e.g., as directed by users of the other programs). Such operations may allow some or all of the previously described types of functionality to be invoked, and include, but are not limited to, the following types of operations: to create, delete, attach, detach, or describe volumes; to create, delete, copy or describe snapshots; to specify access rights or other metadata for volumes and/or snapshots; to manage execution of programs; to provide payment to obtain other types of functionality; to obtain reports and other information about use of capabilities of one or more of the services and/or about fees paid or owed for such use; etc. The operations provided by the API may be invoked by, for example, executing programs on host computing systems of the program execution service and/or by computing systems of customers or other users that are external to the one or more geographical locations used by the block data storage service and/or program execution service.
FIG. 1 illustrates an example of a data center-inclusive environment where speculative reads can be utilized advantageously in accordance with various embodiments. In this example network configuration 100, multiple computing systems 132 are operable to execute various programs, applications, and/or services, and further operable to access reliable non-local block data storage, such as under the control of a block data storage service and/or program execution service, in accordance with various embodiments. In particular, in this example, a program execution service manages the execution of programs on various host computing systems located within a data center 102, and a block data storage service uses multiple other server block data storage systems at the data center to provide reliable non-local block data storage to those executing programs. Multiple remote archival storage systems external to the data center may also be used to store additional copies of at least some portions of at least some block data storage volumes.
In this example, a data center 102 includes a number of racks 104, each rack including a number of host computing devices 106, as well as an optional rack support computing system 134 in this example embodiment. The host computing systems 106 on the illustrated rack 104 each host one or more virtual machines 110 in this example, as well as a distinct node manager module 108 associated with the virtual machines on that host computing system to manage those virtual machines. One or more other host computing systems 116 may also each host one or more virtual machines 110 in this example. Each virtual machine 110 may act as an independent computing node for executing one or more program copies (not shown) for a user (not shown), such as a customer of the program execution service. In addition, this example data center 102 further includes additional host computing systems 114 that do not include distinct virtual machines, but may nonetheless each act as a computing node for one or more programs (not shown) being executed for a user. In this example, a node manager module 112 executing on a computing system (not shown) distinct from the host computing systems 114 and 116 is associated with those host computing systems to manage the computing nodes provided by those host computing systems, such as in a manner similar to the node manager modules 108 for the host computing systems 106. The rack support computing system 134 may provide various utility services for other computing systems local to its rack 102 (e.g., long-term program storage, metering, and other monitoring of program execution and/or of non-local block data storage access performed by other computing systems local to the rack, etc.), as well as possibly to other computing systems located in the data center. Each computing system may also have one or more local attached storage devices (not shown), such as to store local copies of programs and/or data created by or otherwise used by the executing programs, as well as various other components.
In this example, an optional computing system 118 is also illustrated that executes a PES System Manager module for the program execution service to assist in managing the execution of programs on the computing nodes provided by the host computing systems located within the data center (or optionally on computing systems located in one or more other data centers 128, or other remote computing systems 132 external to the data center). As discussed in greater detail elsewhere, a PES System Manager module may provide a variety of services in addition to managing execution of programs, including the management of user accounts (e.g., creation, deletion, billing, etc.); the registration, storage, and distribution of programs to be executed; the collection and processing of performance and auditing data related to the execution of programs; the obtaining of payment from customers or other users for the execution of programs; etc. In some embodiments, the PES System Manager module may coordinate with the node manager modules 108 and 112 to manage program execution on computing nodes associated with the node manager modules, while in other embodiments the node manager modules may not assist in managing such execution of programs.
In this example the data center 102 also includes a computing system 124 that executes a Block Data Storage (“BDS”) system manager module for the block data storage service to assist in managing the availability of non-local block data storage to programs executing on computing nodes provided by the host computing systems located within the data center (or optionally on computing systems located in one or more other data centers 128, or other remote computing systems 132 external to the data center). In particular, in this example, the data center 102 includes a pool of multiple server block data storage systems 122, which each have local block storage for use in storing one or more volume copies 120. Access to the volume copies 120 is provided over the internal network(s) 126 to programs executing on various computing nodes 110 and 114. As discussed in greater detail elsewhere, a BDS System Manager module may provide a variety of services related to providing non-local block data storage functionality, including the management of user accounts (e.g., creation, deletion, billing, etc.); the creation, use and deletion of block data storage volumes and snapshot copies of those volumes; the collection and processing of performance and auditing data related to the use of block data storage volumes and snapshot copies of those volumes; the obtaining of payment from customers or other users for the use of block data storage volumes and snapshot copies of those volumes; etc. In some embodiments, the BDS System Manager module may coordinate with the node manager modules to manage use of volumes by programs executing on associated computing nodes, while in other embodiments the node manager modules may not be used to manage such volume use. In addition, in other embodiments, one or more BDS System Manager modules may be structured in other manners, such as to have multiple instances of the BDS System Manager executing in a single data center (e.g., to share the management of non-local block data storage by programs executing on the computing nodes provided by the host computing systems located within the data center), and/or such as to have at least some of the functionality of a BDS System Manager module being provided in a distributed manner by software executing on some or all of the server block data storage systems 122 (e.g., in a Peer to-peer manner, without any separate centralized BDS System Manager module on a computing system 124).
In this example, the various host computing systems, server block data storage systems, and computing systems are interconnected via one or more internal networks 126 of the data center, which may include various networking devices (e.g., routers, switches, gateways, etc.) that are not shown. In addition, the internal networks 126 are connected to an external network 130 (e.g., the Internet or other public network) in this example, and the data center 102 may further include one or more optional devices (not shown) at the interconnect between the data center and an external network (e.g., network proxies, load balancers, network address translation devices, etc.). In this example, the data center 102 is connected via the external network 130 to one or more other data centers 128 that each may include some or all of the computing systems and storage systems illustrated with respect to data center 102, as well as other remote computing systems 132 external to the data center. The other computing systems 132 may be operated by various parties for various purposes, such as by the operator of the data center or third parties (e.g., customers of the program execution service and/or of the block data storage service). In addition, one or more of the other computing systems may be archival storage systems (e.g., as part of a remote network-accessible storage service) with which the block data storage service may interact, such as under control of one or more archival manager modules (not shown) that execute on the one or more other computing systems or instead on one or more computing systems of the data center, as described in greater detail elsewhere. Furthermore, while not illustrated here, in at least some embodiments, at least some of the server block data storage systems 122 may further be interconnected with one or more other networks or other connection mediums, such as a high-bandwidth connection over which the server storage systems 122 may share volume data (e.g., for purposes of replicating copies of volumes and/or maintaining consistency between primary and mirror copies of volumes), with such a high-bandwidth connection not being available to the various host computing systems in at least some such embodiments.
An environment such as that illustrated with respect to FIG. 1 can be used to provide and manage resources shared among various customers. In one embodiment, a virtualized storage system can be provided using a number of data servers, each having a number of storage devices (e.g., storage disks) attached thereto. The storage system can expose the storage to the customers as a Web service, for example. Customers then can submit Web services requests, or other appropriate requests or calls, to allocate storage on those servers and/or access that storage from the instances provisioned for those customers. In certain embodiments, a user is able to access the data volumes of these storage devices as if those storage devices are conventional block devices. Since the data volumes will appear to the customer instances as if each volume is a disk drive or similar block device, the volumes can be addressed with offsets, lengths, and other such conventional block device aspects. Further, such a system can provide what will be referred to herein as “read after write” consistency, wherein data is guaranteed to be able to be read from the data as soon as the data is written to one of these data volumes. Such a system can provide relatively low latency, such as latencies less than about ten milliseconds. Such a system thus in many ways functions as a traditional storage area network (SAN), but with improved performance and scalability. A customer can make a Web service call into an appropriate API of a Web service layer of the system to provision a data volume and attach that volume to a data instance for that customer.
It will be appreciated that the example of FIG. 1 has been simplified for the purposes of explanation, and that the number and organization of host computing systems, server block data storage systems and other devices may be much larger than what is depicted in FIG. 1. For example, as one illustrative embodiment, there may be approximately 4,000 computing systems per data center, with at least some of those computing systems being host computing systems that may each host fifteen virtual machines, and/or with some of those computing systems being server block data storage systems that may each store several volume copies. If each hosted virtual machine executes one program, then such a data center may execute as many as sixty thousand program copies at one time. Furthermore, hundreds or thousands (or more) volumes may be stored on the server block data storage systems, depending on the number of server storage systems, size of the volumes, and number of mirror copies per volume. It will be appreciated that in other embodiments, other numbers of computing systems, programs and volumes may be used.
FIG. 2 illustrates a simplified example environment 200 which shows the flow of data for read requests using a set of components, which in some embodiments can correspond to a subset of the components discussed with respect to FIG. 1. In this example, a client 202 submits a read request to a host device 206 across a network 204, which in this example is an external network such as the Internet, although in other embodiments this could be a portion of an internal network between an application or service and a host, etc. The host 206, in turn, can submit requests for data to at least one block storage system 212 across a second network 220, which can be at least a portion of an internal network (e.g., a network bus). Such an approach can be used to manage the provision and use of reliable non-local block data storage functionality to clients, applications, or other such entities. Non-local block data storage functionality can be provisioned to an application 208 executing on the host computing system 206 and/or on at least some other computing systems, which may be internal to the system or located across the external network 204. The application 208 can have access to one or more block data storage volumes 218 provided by one or more server block data storage systems 212. The host 206 can utilize a node manager or other such component, as discussed above, to manage access of the application 208 executing on the host to at least some of the non-local block data storage volumes 218.
The example environment also includes at least one database 222 configured to store information about the access patterns for the client 202, application 208, and/or other such entities. The database can include information for specific volumes stored on the server block data storage system 212, as well as on any archival storage system (e.g., for use in managing the volumes), and may further store various other information (not shown) about users or other aspects of the block data storage service. In some embodiments, each node manager module on a host computing system 206 can store information about the current volumes attached to the host computing system and used by the executing application(s) on the host computing system, such as to coordinate interactions with the server block data storage systems. While not illustrated here, each host computing system may further include a distinct logical local block data storage device interface for each volume attached to the host computing system and used by a program executing on the computing system, which may further appear to the executing programs as being indistinguishable from one or more other local physically attached storage devices that provide local storage.
Approaches in accordance with various embodiments can leverage the high visibility provided by the virtualized environment such as that illustrated in FIG. 1 or FIG. 2 to create improved read-ahead optimizations, and make those optimizations efficiently, in order to reduce read latency and provide other such advantages. Further, the information can be stored in such a way that the determined patterns and analysis are persistent across restarts or other such events.
For example, FIG. 3( a) illustrates an example of a set of data blocks 300 that can be analyzed in accordance with various embodiments. As should be understood, other types of data groupings in other types of arrangement can be used as well with various embodiments. In this example, each black block indicates the initial block that was read in response to a request from a user, for an application, on a session, or otherwise associated with a particular aspect, entity, etc. In this example, a monitoring system tracks not only the position of the first read for a set of requests, but also the position of each subsequent read. In this example, it can be seen that for each initial read request for a particular user, for example, the following two blocks (illustrated with a patterned fill) were read by subsequent read requests in the set. Such information can be analyzed by a system manager or other such component to attempt to determine patterns in read requests. By analyzing the data from the example of FIG. 3( a), it can be determined that when a user submits a read request for a block of data, the following two blocks will likely be requested in at least one subsequent request.
Once a pattern is determined, that pattern can be applied to future requests. Consider the example of FIG. 3( b) which illustrates a similar example set of data blocks 320. A read request is received from a user for a particular block of data 322. Once the read request is received, a system manager or other such component can analyze the stored information to determine whether a pattern matches an aspect of that request. For example, there might be a pattern that matches a user or application associated with that request, a volume on which the request is being executed, or any other such aspect. In this example, the analysis based on the information from FIG. 3( a) indicates that a pattern matches the initial read request for the block of data 322. Since the pattern indicates that subsequent reads requests likely will request the next two blocks of data 324, the system can go ahead and read those blocks from the block storage (e.g., pull off disk and store in memory) in order to perform time-intensive portions before those subsequent read requests are received.
In some instances, a user or application might submit read requests across multiple volumes, instances, block storage systems, data centers, or other such groupings of data. In at least some embodiments, this information can be aggregated to improve upon pattern determination. For example, FIG. 3( c) illustrates an example wherein a user submits reads against two different sets of data blocks 340, 360. In the first set 340, only two of the three reads have the same subsequent read pattern, which might not be sufficient to perform a speculative read in at least some embodiments. In the second set 360, there are only two data points (e.g., two initial reads with subsequent behavior) which might not provide enough data points for an acceptable prediction. If a system is able to aggregate the behavior with respect to both sets, however, the data might provide enough certainty to enable a pattern to be determined and applied to future reads. In this example, aggregating the data provides five data points, which might be enough to be statistically significant (or at least meet some other threshold) in at least some embodiments. Further, since four of the five instances exhibit similar behavior, a minimum threshold frequency or other such criterion might be met to determine that it is likely advantageous to perform speculative reads according to that determined behavior. In this case, the frequency with which a user reads the next two blocks after an initial read might be sufficient to cause a system to perform speculative reads of two subsequent blocks whenever a read is received for that user.
In at least some embodiments, the threshold at which a pattern can be determined and/or applied can be set by any appropriate entity, such as a user, provider, or system, and can be adjusted based on factors such as load and performance. For example, if the system is under a heavy load then a higher frequency might be required in order to prevent a large number of speculative reads from being made that are not actually needed. Similarly, if the load on the system is light then the system might perform speculative reads for any pattern that can reasonably be interpreted. Many other levels and determinations can be used as well within the scope of the various embodiments.
In some cases, multiple users or applications can submit requests against a common data volume or other grouping of data. For example, the set of data blocks 400 in FIG. 4 shows the behavior of two different users, user A and user B. In some cases, a system can attempt to determine patterns of usage for the data volume as a whole. In this case, it can be seen that each user typically submits subsequent reads for at least the next two data blocks after an initial read request, such that the system might perform speculative reads of the two subsequent blocks for any initial read request received on that volume. In other embodiments, a system might instead attempt to optimize on the behaviors of individual users or applications with respect to that volume. In this example, user A typically reads the next two blocks after an initial read, while user B typically reads the next three blocks. If the system load allows for such optimization, the system can attempt to perform a number of speculative reads for each user or application that is predicted based upon the respective behavior.
In some embodiments where behavior can be analyzed at the block storage system level, patterns can be detected across many different instances. Behavior can be analyzed across different instances for a common customer, assuming in at least some embodiments that the customer instances of a given class will exhibit similar patterns. In some embodiments, an analyzing component of the block storage layer can test one or more strategies or patterns against a percentage of the customer's instances to determine which patterns provide accurate results, performance enhancements, or other such benefits. In certain embodiments, such testing also can be done across customers or applications. For example, certain customers might do a significant amount of online transaction processing with a specific type of database. Building up a set of behavior information for such users can enable the system to make accurate predictions for other customers doing similar types of things, based upon the user performing a type of activity (e.g., online transaction processing) with respect to a certain type of resource (e.g., type of database). In some embodiments, patterns can be applied based at least in part upon other types of information as well, such as information provided by customers about their volumes and/or instances. Such an approach can at least provide a reasonable starting point for speculative reads, which can then be optimized over time for specific users, applications, etc.
Further, approaches in accordance with various embodiments can determine what to do with data for speculative reads based upon factors such as network load and resource capacity. For example, consider again the example of FIG. 2. In this example the client 202 might send a request to a host 206 across an external network which might issue read requests to the block storage system 212. The block storage system might perform one or more speculative reads based upon at least one aspect associated with the read requests, such as a session identifier or client identifier. Based on aspects of the block storage system, internal network, host, or other such component, the block storage system might decide to send the data for the speculative reads across the network to the host 206 to be stored in cache 210 or other such memory, or might decide to perform the reads from the physical storage medium according to an I/O controller 214 for an instance 218, but instead store the data in local cache 216 at the data local instance 218 instead of sending the data over the internal network 220. It should be understood that similar determinations can be used as to whether to send such data over an external network as well, to one or more resources or devices external to the data provider system, etc.
Such an approach can be utilized in various other embodiments as well. The server or device hosting a drive or other data storage mechanism can read the data for the speculative read(s) into memory, and can determine whether to send that data to another destination or store that data locally. If a host is likely to request the data according to the usage pattern, the host has available storage capacity, and the network between the server and the host is not congested, the server might go ahead and send the data for the speculative reads to the host (or to the DOM-0 for the virtual instance, etc.). If, however, the network is congested or the host does not have sufficient storage capacity, for example, the data server might instead store the data locally without sending the data across the network to the host. Since the time spent reading the data from disk into memory is a major source of latency, performing the speculative read but storing locally can still provide a significant reduction in latency for subsequent read requests. When a subsequent read request is received to the data server for data that is already in memory, the data server can quickly send the data to the host without having to wait to read that data from disk or other such storage. In other embodiments, the system might decide to send the information to some intermediate storage or cache to reduce latency where possible.
In some embodiments, a system administrator might configure discrete settings or otherwise adjust the level of aggressiveness of the speculations at different times, for different situations, or for any other appropriate reason. In any situation where a resource has excess capacity and a benefit can be gained by pre-fetching data or otherwise speculating on user behavior, a system or administrator can have the option or ability to utilize as much of that resource as possible while maintaining other functional aspects of the system.
Further, such a system can provide for many types of read-ahead operation, as opposed to a binary “read ahead” or “no read ahead” as in conventional disk-based systems. A number of different prediction algorithms can be used to determine which blocks to be pre-fetched, how far out to fetch the data, reads for which to fetch data, etc. In addition to pattern detection algorithms such as “most recently used” or “most recently modified” algorithms, various other algorithms can be used as well that can take into account various other types of input. For example, a system can perform Bayesian analysis or utilize another such statistical inference algorithm. In some embodiments, multiple speculative read approaches can be attempted concurrently in order to determine an effectiveness of each approach under certain circumstances, for certain aspects, etc. In some embodiments, the algorithms can be used to predict behavior but not actually applied until the results are analyzed and a level of performance or accuracy for each determined. For example, the predictions and actual requests can be stored, and then analyzed to determine the optimal approach for subsequent reads, etc. Further, unlike reads at the operating system or disk level, such an approach can enable different read approaches to be made for different types or sources of traffic across the same virtual drive, instance, etc.
As discussed, certain embodiments can implement the speculative nature in the DOM-0, which can provide a substantial amount of information at the host level. Further, storing information in a location away from the block storage device enables the information to be recovered in the event of a failure or restart. The pattern information and analysis can be thought of as metadata associated with the block storage volume instead of being associated with the host. Having that information available also allows a volume to be attached to a different host or DOM-0 without losing the ability to leverage that information. Further, such an approach enables the expanding of data volumes while still being able to rely on determined access patterns, such as by using access patterns for similar boot volumes, etc.
In some embodiments, it also can be beneficial to know which applications are associated with a given data volume, as a given volume might be accessed by multiple applications each using a different access pattern. Further, it can be beneficial to know the snapshot from which a given volume was created, as patterns for that snapshot also can be applied to each resulting volume. Thus, it can be beneficial to persistently store metadata associated with a body of data and/or associated with the instance type of the application that is going to use the data.
FIG. 5( a) illustrates an example process 500 for determining patterns in read requests that can be used in accordance with at least one embodiment. Although the example process is illustrated as a series of steps, it should be understood that there can be additional, fewer, or alternative steps performed in similar or alternate orders, or in parallel, within the scope of the various embodiments unless otherwise stated. Similarly, such approaches can be used to perform other speculative operations as well in other embodiments. In this example, a user has an application that is scheduled to execute, and thus access data, at certain times. The user might have an application that the user runs once a day to analyze the data for that business day. The user might boot up the application at the end of the work day to analyze data on a data volume, and when the analysis is complete the user terminates the application. As part of the process, read data for an application can be stored for a first period of time 502. In this example, a single record of every disk block that was read can be persisted after the first run of the application on the data for a given day. The persisted data can be analyzed to attempt to locate patterns in the data 504. Any appropriate pattern matching or data analysis algorithm can be used for the analysis as discussed elsewhere herein. For each pattern, a level of certainty, frequency, or other such metric can be determined 506. For example, the application might be determined to read data blocks in groups of three at least 75% of the time, and blocks in groups of at least two at least 90% of the time. If any of the patterns is determined to at least meet a minimum certainty threshold or other such criterion 508, that pattern can be associated with the application 510 and stored in an appropriate persistent storage for subsequent use 512. In some embodiments additional information can be captured in the pattern as well. For example, if the application reads the next adjacent block half a second after the previous block, then the system might advantageously pre-fetch the data any time within that half-second if conditions are favorable, even if the system might not pre-fetch the data immediately after the previous block due to the conditions at that time. The patterns and/or analysis data in at least some embodiments can be stored as metadata associated with a given data volume. If no patterns are detected, or no pattern at least meets the minimum level of certainty, etc., then no speculative reads might be performed for the next period of application reads 514.
FIG. 5( b) illustrates an example process 550 for using determined patterns to perform speculative reads that can be used in accordance with at least one embodiment. At some time subsequent to a pattern determination such as the one described with respect to FIG. 5( a), another period can begin wherein those patterns can be applied to the read requests for that application. For example, an application can be executed again after the end of the next work day. At some point after the application is re-executed, a read request will be received for that application 552. A system or service receiving the request can access the determined disk access pattern(s) for that application 554 (or client, user, volume, etc.), as may be stored as metadata associated with a particular data volume or other such component. If no pattern is found to match the request 556, the system can return the data for the read request 558 and wait for the next request. If at least a pattern is found to match the request, the system or service can select and use a pattern to anticipate the future actions of the application based at least in part upon the stored metadata. In this example, the system can determine the number of subsequent data blocks the application is likely to request based on past behavior 560, and based on the single received read request can pre-fetch at least one additional data block according to the usage pattern for the application 562. If there are multiple patterns that match, the system or service can select a pattern that is optimal for current network load, session data, or other such information.
In this example, the system or service also can analyze certain existing network conditions or resource state information 564. For example, a system can determine whether a network path between a data volume and a host has excess available bandwidth, and/or whether the host has available excess memory to temporarily store data. Based at least upon whether there are favorable network conditions for sending data 566, the system or service can determine to send the data across the network to a host running the application or an intermediate caching destination between the data source and the application 568, or can determine to store the data at the data volume or host machine with the disk drive, etc. 570. There are many places in various systems where the data can be cached or otherwise stored temporarily, such as on any host device, on a flash device for a data volume, or on a device dedicated to handle caching, etc. If a read request is subsequently received that requires the pre-fetched blocks 572, the application can obtain that data from the temporary storage location 574 (e.g., either host, depending on the current location) without having to wait for the data block(s) to be read from disk. Such an approach can also take into account information such as the bus constraints. By examining the load at the current time, the system can determine whether or not to ship the pre-fetched bits all the way across the respective network to the host machine. If shipping the bits will not affect any other work, the system might decide to undertake that extra effort now, even if it is not certain whether that extra effort will pay off in improved performance. In some embodiments, the system can also determine how to send the data across the network, such as to send in parallel or serialize, segment into smaller packets, wait to fill up an Ethernet frame, etc.
While in many embodiments a storage entity (e.g., a block data storage service or BDS manager) can drive the speculation for multiple users, applications, hosts, or storage entities, for example, in other embodiments an entity outside of the storage entity and any compute entity or node can perform the data analysis and/or direct the speculation. Such an approach enables access patterns and functionality to be separated from the compute and storage entities, such that the patterns and functionality can be shared among new or other such entities or nodes. Further, the lifetime of the access patterns and other such information can extend beyond that of any individual entity, and can be used to predict information for future entities.
FIG. 6 illustrates an example of an environment 600 that can utilize and/or take advantage of aspects in accordance with various embodiments. As will be appreciated, although a Web-based environment is used for purposes of explanation, different environments may be used, as appropriate, to implement various embodiments. The environment 600 shown includes both a testing or development portion (or side) and a production portion. The production portion includes an electronic client device 602, which can include any appropriate device operable to send and receive requests, messages, or information over an appropriate network 604 and convey information back to a user of the device. Examples of such client devices include personal computers, cell phones, handheld messaging devices, laptop computers, set-top boxes, personal data assistants, electronic book readers, and the like. The network can include any appropriate network, including an intranet, the Internet, a cellular network, a local area network, or any other such network or combination thereof. Components used for such a system can depend at least in part upon the type of network and/or environment selected. Protocols and components for communicating via such a network are well known and will not be discussed herein in detail. Communication over the network can be enabled by wired or wireless connections, and combinations thereof. In this example, the network includes the Internet, as the environment includes a Web server 606 for receiving requests and serving content in response thereto, although for other networks an alternative device serving a similar purpose could be used as would be apparent to one of ordinary skill in the art.
The illustrative environment includes at least one application server 608 and a data store 610. It should be understood that there can be several application servers, layers, or other elements, processes, or components, which may be chained or otherwise configured, which can interact to perform tasks such as obtaining data from an appropriate data store. As used herein the term “data store” refers to any device or combination of devices capable of storing, accessing, and retrieving data, which may include any combination and number of data servers, databases, data storage devices, and data storage media, in any standard, distributed, or clustered environment. The application server can include any appropriate hardware and software for integrating with the data store as needed to execute aspects of one or more applications for the client device, handling a majority of the data access and business logic for an application. The application server provides access control services in cooperation with the data store, and is able to generate content such as text, graphics, audio, and/or video to be transferred to the user, which may be served to the user by the Web server in the form of HTML, XML, or another appropriate structured language in this example. The handling of all requests and responses, as well as the delivery of content between the client device 602 and the application server 608, can be handled by the Web server. It should be understood that the Web and application servers are not required and are merely example components, as structured code discussed herein can be executed on any appropriate device or host machine as discussed elsewhere herein. Further, the environment can be architected in such a way that a test automation framework can be provided as a service to which a user or application can subscribe. A test automation framework can be provided as an implementation of any of the various testing patterns discussed herein, although various other implementations can be used as well, as discussed or suggested herein.
The environment also includes a development and/or testing side, which includes a user device 618 allowing a user such as a developer, data administrator, or tester to access the system. The user device 618 can be any appropriate device or machine, such as is described above with respect to the client device 602. The environment also includes a development server 620, which functions similar to the application server 608 but typically runs code during development and testing before the code is deployed and executed on the production side and is accessible to outside users, for example. In some embodiments, an application server can function as a development server, and separate production and testing storage may not be used.
The data store 610 can include several separate data tables, databases, or other data storage mechanisms and media for storing data relating to a particular aspect. For example, the data store illustrated includes mechanisms for storing production data 612 and user information 616, which can be used to serve content for the production side. The data store also is shown to include a mechanism for storing testing data 614, which can be used with the user information for the testing side. It should be understood that there can be many other aspects that may need to be stored in the data store, such as for page image information and access right information, which can be stored in any of the above listed mechanisms as appropriate or in additional mechanisms in the data store 610. The data store 610 is operable, through logic associated therewith, to receive instructions from the application server 608 or development server 620, and obtain, update, or otherwise process data in response thereto. In one example, a user might submit a search request for a certain type of item. In this case, the data store might access the user information to verify the identity of the user, and can access the catalog detail information to obtain information about items of that type. The information then can be returned to the user, such as in a results listing on a Web page that the user is able to view via a browser on the user device 602. Information for a particular item of interest can be viewed in a dedicated page or window of the browser.
The environment in one embodiment is a distributed computing environment utilizing several computer systems and components that are interconnected via communication links, using one or more computer networks or direct connections. However, it will be appreciated by those of ordinary skill in the art that such a system could operate equally well in a system having fewer or a greater number of components than are illustrated in FIG. 6. Thus, the depiction of the system 600 in FIG. 6 should be taken as being illustrative in nature, and not limiting to the scope of the disclosure.
An environment such as that illustrated in FIG. 6 can be useful for a provider such as an electronic marketplace, wherein multiple hosts might be used to perform tasks such as serving content, authenticating users, performing payment transactions, or performing any of a number of other such tasks. Some of these hosts may be configured to offer the same functionality, while other servers might be configured to perform at least some different functions. The electronic environment in such cases might include additional components and/or other arrangements, such as those illustrated in the configuration 100 of FIG. 1, discussed in detail above.
wherein a location of the temporary storage is determined based at least in part upon at least one usage aspect of a network containing the data store.
sending the data for the at least one speculative read request to a data cache between the data store and the at least one source when the at least one aspect of the at least one network resource at least meets a minimum availability value.
3. The computer-implemented method of claim 1, wherein the data for the at least one speculative read request is sent to a cache on a host machine executing an application, the application being one of the at least one source.
4. The computer-implemented method of claim 1, wherein the at least one data store includes network-attached storage.
6. The computer-implemented method of claim 5, wherein the data store is a block data storage system.
7. The computer-implemented method of claim 5, wherein the source is at least one of a user, client, or application.
8. The computer-implemented method of claim 5, wherein the at least one usage aspect of the network includes at least one of bandwidth, latency, throughput, and available memory.
9. The computer-implemented method of claim 5, wherein the data store further comprises at least one of network-attached storage or virtualized operating system instances utilizing attached network storage.
10. The computer-implemented method of claim 5, wherein the at least one access pattern is determined based at least in part upon prior read requests received for at least one of multiple sources and multiple data stores.
determining whether to perform the at least one speculative read based at least in part upon at least one network performance criterion.
12. The computer-implemented method of claim 5, wherein the read request is received for a first user, and the at least one access pattern is determined based at least in part upon prior read requests from at least one second user.
aggregating read request data from multiple data sources for pattern analysis.
concurrently testing multiple pattern recognition algorithms against data stored in the data store before selecting an access pattern that matches at least one aspect of the analyzed read requests.
15. The computer-implemented method of claim 5, wherein access pattern information is persistently stored as metadata associated with a block data storage volume.
storing a single record of every disk block that was read in response to a read request for use in pattern matching.
18. The computer-implemented method of claim 17, wherein the entity is operable to store information for the at least one access pattern in a location independent of the compute node and the storage node.
19. The computer-implemented method of claim 17, wherein the at least one access pattern is able to be shared among a plurality of compute nodes and storage nodes.
20. The computer-implemented method of claim 19, wherein the at least one access pattern is able to have a lifetime that exceeds a lifetime of any of the compute nodes and storage nodes.
21. The computer-implemented method of claim 19, wherein the at least one access pattern is able to be used to predict information for future compute nodes and storage nodes.
23. The system according to claim 22, wherein the data store further comprises at least one of network-attached storage or virtualized operating system instances utilizing attached network storage.
24. The system according to claim 22, wherein the at least one access pattern is determined based at least in part upon prior read requests received for at least one of multiple sources and multiple data stores.
26. The non-transitory computer-readable storage medium according to claim 25, wherein the data store further comprises at least one of network-attached storage or virtualized operating system instances utilizing attached network storage.