US20160132263A1 - Storing data across a plurality of storage nodes - Google Patents
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- US20160132263A1 US20160132263A1 US15/000,718 US201615000718A US2016132263A1 US 20160132263 A1 US20160132263 A1 US 20160132263A1 US 201615000718 A US201615000718 A US 201615000718A US 2016132263 A1 US2016132263 A1 US 2016132263A1
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Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for storing data on storage nodes. In one aspect, a method includes receiving a file to be stored across a plurality of storage nodes each including a cache. The is stored by storing portions of the file each on a different storage node. A first portion is written to a first storage node's cache until determining that the first storage node's cache is full. A different second storage node is selected in response to determining that the first storage node's cache is full. For each portion of the file, a location of the portion is recorded, the location indicating at least a storage node storing the portion.
Description
- This application is a continuation of U.S. patent application Ser. No. 14/293,330, filed on Jun. 2, 2014, entitled “Storing Data Across A Plurality Of Storage Nodes,” which is a continuation of U.S. patent application Ser. No. 13/010,548, filed on Jan. 20, 2011, now U.S. Pat. No. 8,745,329, entitled “Storing Data Across A Plurality Of Storage Nodes,” the disclosures of which are considered part of and incorporated herein by reference.
- This specification relates to storing digital data on physical storage devices.
- Data striping involves storing data across an array of storage devices. Rather than writing all the data to a single device, the data is written in parallel to multiple devices so that the overall rate of data being written is greater than is possible with only a single device. Data striping can be combined with other data storage techniques, for example, storing redundant data, error-detecting, or error-correcting codes with the data to create fast, reliable storage.
- A computer storage system stores a file on an array of storage nodes. Each storage node includes one or more storage devices, and each storage node includes a cache (e.g., Random Access Memory (RAM)) and slower storage (e.g., a hard disk.) The cache can store data faster than the slower storage. For example, a storage node can be a hard disk with caching capabilities. The computer storage system writes data from the file to a first storage node until the first storage node's cache is full and then writes data from the file to a second storage node. The first storage node copies the data from the first storage node's cache to the first storage node's slower storage. The computer storage system continues writing data to various storage devices so that the file is completely stored. Data written to storage nodes can be written in the same manner regardless of whether the storage nodes are attached to, e.g., an individual computer or multiple data processing apparatus.
- In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving a file to be stored across a plurality of storage nodes each including a cache; storing the file by storing a plurality of portions of the file each on a different storage node, including writing a first portion to a first storage node's cache until determining that the first storage node's cache is full and selecting a different second storage node in response to determining that the first storage node's cache is full; and recording, for each portion of the file, a location of the portion, the location indicating at least a storage node storing the portion. Other embodiments of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.
- These and other embodiments can optionally include one or more of the following features. Determining that the first storage node's cache is full comprises determining that a data storage rate for writing the first portion has dropped. Determining that the first storage node's cache is full comprises comparing an amount of the first portion to a known size of the first storage node's cache and determining that the amount is equal to or exceeds the known size. Selecting the second storage node comprises selecting the second storage node randomly from the storage nodes. Selecting the second storage node comprises selecting a storage node having a write time furthest in the past. The actions further include assigning a weight to each of the plurality of storage nodes, and wherein selecting the second storage node includes using the weights. Each storage node's weight is based on a performance characteristic of the storage node. Selecting the second storage node is based on one or more of: a performance characteristic of the second storage node, usage history of the second storage node, and a past performance record for the second storage node. Selecting the second storage node is based on one or more of: an absolute amount of data stored by the second storage node, and an amount of data stored by the second storage node relative to a capacity of the second storage node. The first storage node copies the first portion from the first storage node's cache to slower storage in response to writing the first portion to the first storage node's cache. Storing the file comprises writing a second portion to the second storage node. Writing the second portion to the second storage node comprises writing the second portion to the second storage node's cache until determining that second storage node's cache is full. Storing the file comprises continuing to write portions of the file to various storage nodes until the file is completely stored in the storage nodes. The actions further include receiving a request for the file; reading the first portion from the first storage node; and reading the second portion from the second storage node.
- Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. A computer storage system can increase its storage speed by writing to storage nodes with caches. The storage nodes are kept in a cached mode more frequently to decrease latency and increase throughput. The computer storage system can recover the stored data using a storage log.
- The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
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FIG. 1 is a block diagram of an example storage system. -
FIG. 2 is a flow diagram of an example technique for storing data on an array of storage nodes each having a cache. -
FIG. 3 is a flow diagram of an example technique for storing data on an array of storage nodes each having a cache. -
FIG. 4 is a flow diagram of an example technique for reading data from an array of storage nodes each having a cache. -
FIG. 5 is a schematic diagram of an example computing system for storing data. - Like reference numbers and designations in the various drawings indicate like elements.
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FIG. 1 is a block diagram of anexample storage system 102. Thestorage system 102 includes one or more data processing apparatus that can be distributed geographically. Thestorage system 102 stores files of digital data on an array of storage nodes 110 a-d. Thestorage system 102 can receive an electronic file (or “file”) from aclient device 104 over acomputer network 106 or from another source. A file can be any data, for example, text, an image, audio content, video content, or combinations thereof. A file can be received by thestorage system 102 as through a network file system protocol, for example. Other protocols are possible. Thestorage system 102 can create data for storage, e.g., by producing service logs during normal operation. Thestorage system 102 can create data for storage based on other data, e.g., by producing a small thumbnail image from a high-resolution source image. Astorage engine 108 determines how to distribute a file's contents for storage on the storage nodes 110 a-d. - Each storage node 110 includes a cache 112 and one or more slower storage 114 devices. The cache 112 can be, for example, RAM, flash memory, a hard disk, or the like. The slower storage 114 can also be RAM or a hard disk, but the slower storage 114 takes longer to store data than the cache 112. The slower storage 114 typically has greater storage capacity than the cache 112. Data written to a storage node 110 is first written to the cache 112 because data can be stored in the cache 112 faster than the slower storage 114. The storage node 110 then copies the data from the cache 112 to the slower storage 114.
- For example, a storage node 110 can be a hard disk with a RAM cache. Data written to the storage node 110 is first written to the RAM cache and then copied to the disk. In another example, a storage node 110 is an array of hard disks with a shared RAM cache, e.g., a Redundant Array of Inexpensive Disks (RAID) device. Although the storage node 110 includes multiple disks, the
storage engine 108 can logically address the storage node as a single device. - The
storage engine 108 stores a file by writing a portion of data from the file to a storage node. Thestorage engine 108 writes data to that storage node until determining that the storage node's cache is full. A cache is full when it is storing an amount of data at or near its capacity so that it can no longer accept new data for storage. Techniques for determining that a cache is full are described below. Thestorage engine 108 then writes another portion of data from the file to a different storage node until determining that the different storage node's cache is full. The portions written by thestorage engine 108 are not necessarily adjacent in the file, and the file's contents do not have to be written in order. - The
storage engine 108 continues to write data from the file on various storage nodes until the file's contents are completely written. Alternatively, less than the entire file's content is written. If all of the storage nodes have full caches, thestorage engine 108 can wait until a cache has free space (after copying its contents to slower storage) or write directly to slower storage. In some implementations, thestorage engine 108 continues selecting new storage nodes to write to until it finds one that is available, regardless of whether or not all of the storage nodes have full caches. By writing to storage nodes with available cache storage, thestorage engine 108 increases its rate of data storage compared to writing a single file to a single device regardless of whether the cache is full or not. Thestorage engine 108 tracks the location of written data from the file in astorage log 116. Thestorage log 116 includes information necessary to recover the file from the storage nodes. For example, thestorage log 116 can include the location of data on the first storage node. Thestorage log 116 also includes an association between the location and the file (e.g., by including the location and the file name in a same record or row of a table). An example storage log is illustrated below in Table 1. -
TABLE 1 Filename Storage Node Addresses Timestamp File 1 A 1-50 1 B 200-300 2 C 50-125 3 File 2 A 150-250 4 C 200-300 5 - For example, consider an example scenario for storing “File 1” as illustrated by Table 1. The first three rows are associated with File 1 by the first column. The first row indicates that at the time given by timestamp 1, the
storage engine 108 wrote data from File 1 to addresses 1-50 of storage node A. The second row indicates that at the time given by timestamp 2, thestorage engine 108 wrote data from File 1 to addresses 200-300 of storage node B. Thestorage engine 108 used more addresses at storage node B than storage node A, for example, because storage node B has a larger cache than storage node A, or because storage node A's cache was partially full when thestorage engine 108 began writing data. The third row indicates that at the time given by timestamp 3, thestorage engine 108 wrote data from File 1 to addresses 50-125 of storage node C. - The addresses illustrated by Table 1 are example addresses. In general, the addresses refer to locations of data on slower storage at a storage node. Even though data is typically initially written to a cache, the addresses refer to locations on slower storage because the storage node copies data from the cache to slower storage. For example, a storage node can map the location of data written to its cache to a destination location on slower storage.
- The
storage engine 108 can determine that a storage node's cache is full using various techniques. For example, thestorage engine 108 can monitor the rate at which a storage node is accepting data. When the rate drops, thestorage engine 108 determines that the storage node's cache is full. In another example, thestorage engine 108 can compare the amount of data it has written to a storage node to a specified size of the storage node's cache. When the amount of data written exceeds the specified size, thestorage engine 108 determines that the storage node's cache is full. Thestorage engine 108 can store specified sizes for the storage nodes, or thestorage engine 108 can query a storage node for a specified size before it begins writing to the storage node. In some implementations, no determination is needed because thestorage engine 108 writes only the amount of a specified size to a storage node and then selects a different storage node. Those implementations are useful, for example, where there is enough cache storage to assume that each cache will be empty when selected again. - When the
storage engine 108 determines that a storage node's cache is full, it can select a different storage node using various techniques. Consider the following four example techniques. - In a first example, the
storage engine 108 can randomly select another storage node. This is useful, for example, where multiple storage engines are writing to the various storage nodes and it is difficult to determine whether any given storage node will have available cache storage. - In a second example, the
storage engine 108 can use thestorage log 116 to select a different storage node. Thestorage engine 108 typically selects a storage node having a write time furthest in the past of any storage node. For example, consider the example storage log illustrated in Table 1. Of storage nodes A, B, and C, storage node B was has a write time furthest in the past (at timestamp 2). - In a third example, the
storage engine 108 can use weighted randomization. Weighted randomization involves assigning weights to storage nodes and selecting storage nodes based on the weights. For example, suppose all the storage nodes belong to one of two classes, A and B. The nodes belonging to class B are, on average, two times faster than nodes belonging to class A (e.g., because nodes belonging to class B use newer hardware). Thestorage engine 108 can select nodes from class B more frequently than nodes from class A to improve system performance. In some implementations, weights W[k] are assigned to individual storage nodes, and thestorage engine 108 selects storage nodes so that the likelihood of choosing a node #k is proportional to -
- In a fourth example,
storage engine 108 can select nodes in a pseudo-random manner. The likelihood of a node being chosen is based on various factors related to the node, e.g., performance characteristics (e.g., cache speed), usage history (e.g., using the storage log 116), past performance records (e.g., avoiding a node that consistently or temporarily is slower than other nodes). - The
storage engine 108 can use other information in selecting a different storage node. Thestorage engine 108 can select storage nodes according to a plan or scheme for storage. For example, thestorage engine 108 can use the absolute amount of data already stored by a storage node, the amount of data stored by a storage node relative to its capacity (e.g., to prevent writing or attempting to write to node that is full), the age of data (e.g., where new data is accessed more frequently than old data), and so on. Thestorage engine 108 can use a storage node's load and capacity, for example, to facilitate uniform storage utilization across all available storages and avoid having all the data stored on very few nodes. - In another example, the
storage engine 108 can balance speed and latency of writing data against data processing capacity, e.g., by monitoring data access patterns, current or projected. This is useful, for example, to avoid storing a large amount of high-demand data on a single storage node or just a few storage nodes. Thestorage engine 108 can identify high-demand data, for example, using past data access patterns of the specific data, or using other information about the data, e.g., whether it is freshly generated.FIG. 2 is a flow diagram of anexample technique 200 for storing data on an array of storage nodes each having a cache. Thetechnique 200 can be performed by a computer system, e.g., thecomputer system 102 ofFIG. 1 . For purposes of illustration, thetechnique 200 will be described with respect to a system that performs the technique. - The system identifies a file for storage on the storage nodes (step 202). For example, the system can receive a file from a client device, e.g., the
client device 104 ofFIG. 1 . - The system selects a storage node (step 204). For example, the system can randomly select a storage node, or the system can select a storage node using a storage log, e.g., the
storage log 116 ofFIG. 1 . - The system stores data from the file at the selected storage node (step 206). For example, the system can send a fixed amount of data to the selected storage node, or the system can stream data for a fixed amount of time.
- The system records the location of the stored data at the selected storage node (step 208). For example, the system can record the location in a storage log, e.g., the
storage log 116 ofFIG. 1 . The system associates the location of the stored data with the file (e.g., using a file name or file descriptor). - The system determines whether the file is completely stored (step 210). If the file is stored, then the system identifies another file (return to step 202). If the file is not stored, then the system determines whether the selected storage node's cache is full (step 212). For example, the system can monitor the data rate of the write to the storage node, or compare a specified size of the storage node to the amount of data written.
- If the selected storage node's cache is full, then the system selects another storage node (return to step 204). If the selected storage node's cache is not full, then the system continues writing data from the file to the selected storage node (return to step 206).
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FIG. 3 is a flow diagram of anexample technique 300 for storing data on an array of storage nodes each having a cache. Thetechnique 300 can be performed by a computer system, e.g., thecomputer system 102 ofFIG. 1 . For purposes of illustration, thetechnique 300 will be described with respect to a system that performs the technique. - The system receives a file (step 302). The system writes a portion of data from the file to a storage node's cache until determining that the storage node's cache is full (step 304). In some implementations, the storage node copies the portion of data from the storage node's cache to slower storage in response to writing the portion of data to the storage node's cache. After data is copied to slower storage, the cache locations storing that data can be, for example, wiped clean (e.g., zeroed out) or marked as available for writing.
- In some implementations, determining that the storage node's cache is full includes determining that a data storage rate has dropped. In some implementations, determining that the storage node's cache is full includes comparing an amount of data to a known size of the storage node's cache and determining that the amount exceeds the known size. The system records a location of the portion of data on the storage node and associates the location with the file (step 306).
- The system selects a different storage node in response to determining that the storage node's cache is full (step 308). In some implementations, selecting the different storage node includes selecting the different storage node randomly from the storage nodes. In some implementations, selecting the different storage node includes accessing a log of write times and storage nodes and selecting the storage node having the write time furthest in the past. The system writes another portion of data from the file to the different storage node (step 310). In some implementations, writing another portion of data from the file to the different storage node includes writing the data to the different storage node's cache until determining that the different storage node's cache is full. The system records a location of the data on the different storage node and associates the location with the file (step 312).
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FIG. 4 is a flow diagram of anexample technique 400 for reading data from an array of storage nodes each having a cache. Thetechnique 400 can be performed by a computer system, e.g., thecomputer system 102 ofFIG. 1 . For purposes of illustration, thetechnique 400 will be described with respect to a system that performs the technique. - The system receives a request for a file (step 402). For example, the requester can be a client device, e.g., the
client device 104 ofFIG. 1 . The system identifies a storage node having data from the file (step 404). For example, the system can access a storage log having locations of data for various files, e.g., thestorage log 116 ofFIG. 1 . The system reads the data from the identified storage node (step 406). Typically, the system reads the data from the slower storage of the storage node because the storage node has copied data from its cache to its slower storage; however, in some cases, the system can read the data from the cache to improve performance (e.g., where the data was recently written to the cache and has not yet been overwritten or zeroed out). - The system determines whether all the data in the file has been read (step 408). For example, the system can determine whether there are addition entries for the file in a storage log, or compare the amount of data read with a size of the file (e.g., received with the request or stored in a storage log). If all the data for the file has been read, the system provides the data from the file to the requester (step 410). If all the data for the file has not been read, the system identifies another storage node having data from the file (return to step 404).
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FIG. 5 is a schematic diagram of anexample computing system 502 for storing data. In operation, thesystem 502 communicates with one ormore storage nodes 590, directly or through anetwork 580. - The
system 502 includes one or more data processing apparatus. While only one data processing apparatus is shown inFIG. 5 , multiple data processing apparatus can be used. Thesystem 502 can be distributed geographically. For example, thesystem 502 can include multiple data processing apparatus in various geographic locations. - The
system 502 includes various modules, e.g., modules of computer program instructions, including awriting engine 504 to write data to the storage nodes (e.g., using thetechnique 200 ofFIG. 2 ); areading engine 506 to read data from the storage nodes (e.g., using thetechnique 400 ofFIG. 4 ); and a log engine to manage a storage log (e.g., thestorage log 116 ofFIG. 1 ). - Each module is configured to run on the
system 502. For example, a module can run as part of an operating system on thesystem 502, as an application on thesystem 502, or as part of the operating system and part of an application on thesystem 502. Although several software modules are illustrated, the functionality of the server can be implemented in fewer or more software modules. Moreover, the software modules can be distributed on one or more data processing apparatus connected by one or more networks or other suitable communication mediums. - The
system 502 also includes hardware or firmware devices including one ormore processors 512, one or moreadditional devices 514, a computerreadable medium 516, acommunication interface 518, and one or moreuser interface devices 520. Eachprocessor 512 is capable of executing instructions for execution within thesystem 502. Eachprocessor 512 is capable of executing instructions stored on the computerreadable medium 516 or on a storage device such as one of theadditional devices 514. Thesystem 502 uses itscommunication interface 518 to communicate with one ormore computers 590, for example, over anetwork 580. Examples ofuser interface devices 520 include a display, a camera, a speaker, a microphone, a tactile feedback device, a keyboard, and a mouse. Thesystem 502 can store instructions that implement operations associated with the modules described above, for example, on the computerreadable medium 516 or one or moreadditional devices 514, for example, one or more of a floppy disk device, a hard disk device, an optical disk device, or a tape device. - Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).
- The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.
- The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.
- A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
- The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
- Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
- To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
- Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
- The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.
- While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what can be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features can be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination can be directed to a subcombination or variation of a subcombination.
- Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
- Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.
Claims (21)
1-29. (canceled)
30. A computer-implemented method comprising:
receiving a file to be stored across a plurality of storage nodes;
storing one or more portions of the file on one or more of the plurality of storage nodes;
identifying a data access pattern associated with the file;
determining a quantity of one or more additional storage nodes of the plurality of storage nodes for storage of the file based on the identified data access pattern; and
storing additional portions of the file on the one more additional storage nodes.
31. The method of claim 30 , wherein identifying the data access pattern associated with the file includes monitoring a current data access pattern of stored portions of the file.
32. The method of claim 30 , wherein identifying the data access pattern associated with the file includes identifying a projected data access pattern of the file.
33. The method of claim 30 , wherein identifying the data access pattern associated with the file includes identifying a previous data access pattern of the file prior to the current storing of the file.
34. The method of claim 30 , wherein determining the quantify of the one or more additional storage nodes of the plurality of storage nodes for storage of the file is further based on a timestamp associated with one or more portions of the file.
35. The method of claim 30 , further comprising classifying the file as high-demand based on the identified data access pattern, and in response, storing the additional portion of the file on at least two or more of the additional storage nodes.
36. The method of claim 30 , wherein storing the additional portions of the file on the one or more additional storage nodes further comprises minimizing storing a threshold quantity of portions of the file on two or fewer of the additional storage nodes.
37. A system comprising:
one or more computers; and
a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
receiving a file to be stored across a plurality of storage nodes;
storing one or more portions of the file on one or more of the plurality of storage nodes;
identifying a data access pattern associated with the file;
determining a quantity of one or more additional storage nodes of the plurality of storage nodes for storage of the file based on the identified data access pattern; and
storing additional portions of the file on the one more additional storage nodes.
38. The system of claim 37 , wherein identifying the data access pattern associated with the file includes monitoring a current data access pattern of stored portions of the file.
39. The system of claim 37 , wherein identifying the data access pattern associated with the file includes identifying a projected data access pattern of the file.
40. The system of claim 37 , wherein identifying the data access pattern associated with the file includes identifying a previous data access pattern of the file prior to the current storing of the file.
41. The system of claim 37 , wherein determining the quantify of the one or more additional storage nodes of the plurality of storage nodes for storage of the file is further based on a timestamp associated with one or more portions of the file.
42. The system of claim 37 , the operations further comprising classifying the file as high-demand based on the identified data access pattern, and in response, storing the additional portion of the file on at least two or more of the additional storage nodes.
43. The system of claim 37 , wherein storing the additional portions of the file on the one or more additional storage nodes further comprises minimizing storing a threshold quantity of portions of the file on two or fewer of the additional storage nodes.
44. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:
receiving a file to be stored across a plurality of storage nodes;
storing one or more portions of the file on one or more of the plurality of storage nodes;
identifying a data access pattern associated with the file;
determining a quantity of one or more additional storage nodes of the plurality of storage nodes for storage of the file based on the identified data access pattern; and
storing additional portions of the file on the one more additional storage nodes.
45. The computer storage medium of claim 44 , wherein identifying the data access pattern associated with the file includes monitoring a current data access pattern of stored portions of the file.
46. The computer storage medium of claim 44 , wherein identifying the data access pattern associated with the file includes identifying a projected data access pattern of the file.
47. The computer storage medium of claim 44 , wherein identifying the data access pattern associated with the file includes identifying a previous data access pattern of the file prior to the current storing of the file.
48. The computer storage medium of claim 44 , wherein determining the quantify of the one or more additional storage nodes of the plurality of storage nodes for storage of the file is further based on a timestamp associated with one or more portions of the file.
49. The computer storage medium of claim 44 , the operations further comprising classifying the file as high-demand based on the identified data access pattern, and in response, storing the additional portion of the file on at least two or more of the additional storage nodes.
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10713276B2 (en) * | 2016-10-03 | 2020-07-14 | Ocient, Inc. | Data transition in highly parallel database management system |
Families Citing this family (39)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8533343B1 (en) | 2011-01-13 | 2013-09-10 | Google Inc. | Virtual network pairs |
US9135037B1 (en) | 2011-01-13 | 2015-09-15 | Google Inc. | Virtual network protocol |
US8874888B1 (en) | 2011-01-13 | 2014-10-28 | Google Inc. | Managed boot in a cloud system |
US8745329B2 (en) | 2011-01-20 | 2014-06-03 | Google Inc. | Storing data across a plurality of storage nodes |
US8812586B1 (en) | 2011-02-15 | 2014-08-19 | Google Inc. | Correlating status information generated in a computer network |
US8261295B1 (en) | 2011-03-16 | 2012-09-04 | Google Inc. | High-level language for specifying configurations of cloud-based deployments |
US9063818B1 (en) | 2011-03-16 | 2015-06-23 | Google Inc. | Automated software updating based on prior activity |
US8533796B1 (en) | 2011-03-16 | 2013-09-10 | Google Inc. | Providing application programs with access to secured resources |
US9237087B1 (en) | 2011-03-16 | 2016-01-12 | Google Inc. | Virtual machine name resolution |
US9075979B1 (en) | 2011-08-11 | 2015-07-07 | Google Inc. | Authentication based on proximity to mobile device |
US8966198B1 (en) | 2011-09-01 | 2015-02-24 | Google Inc. | Providing snapshots of virtual storage devices |
US9069616B2 (en) | 2011-09-23 | 2015-06-30 | Google Inc. | Bandwidth throttling of virtual disks |
CN105897859B (en) * | 2016-03-25 | 2021-07-30 | 北京书生云科技有限公司 | Storage system |
US8958293B1 (en) | 2011-12-06 | 2015-02-17 | Google Inc. | Transparent load-balancing for cloud computing services |
US9178698B1 (en) | 2011-12-21 | 2015-11-03 | Google Inc. | Dynamic key management |
US8983860B1 (en) | 2012-01-30 | 2015-03-17 | Google Inc. | Advertising auction system |
US8996887B2 (en) | 2012-02-24 | 2015-03-31 | Google Inc. | Log structured volume encryption for virtual machines |
US8677449B1 (en) | 2012-03-19 | 2014-03-18 | Google Inc. | Exposing data to virtual machines |
US9069806B2 (en) | 2012-03-27 | 2015-06-30 | Google Inc. | Virtual block devices |
US9847907B2 (en) | 2012-11-26 | 2017-12-19 | Amazon Technologies, Inc. | Distributed caching cluster management |
US9602614B1 (en) | 2012-11-26 | 2017-03-21 | Amazon Technologies, Inc. | Distributed caching cluster client configuration |
US9529772B1 (en) * | 2012-11-26 | 2016-12-27 | Amazon Technologies, Inc. | Distributed caching cluster configuration |
US9262323B1 (en) | 2012-11-26 | 2016-02-16 | Amazon Technologies, Inc. | Replication in distributed caching cluster |
US9430255B1 (en) | 2013-03-15 | 2016-08-30 | Google Inc. | Updating virtual machine generated metadata to a distribution service for sharing and backup |
CN103678159B (en) * | 2013-12-27 | 2017-02-22 | 威盛电子股份有限公司 | Data storage device and data write-in method thereof |
US20150271096A1 (en) * | 2014-03-24 | 2015-09-24 | Google Technology Holdings LLC | Allocation of Client Device Memory for Content from Content Sources |
EP3201778A4 (en) * | 2014-10-03 | 2018-04-25 | Agency for Science, Technology and Research | Method for optimizing reconstruction of data for a hybrid object storage device |
US10031679B2 (en) * | 2014-11-21 | 2018-07-24 | Security First Corp. | Gateway for cloud-based secure storage |
US10572443B2 (en) | 2015-02-11 | 2020-02-25 | Spectra Logic Corporation | Automated backup of network attached storage |
US20160349993A1 (en) * | 2015-05-29 | 2016-12-01 | Cisco Technology, Inc. | Data-driven ceph performance optimizations |
US11314648B2 (en) * | 2017-02-08 | 2022-04-26 | Arm Limited | Data processing |
CN110069210B (en) * | 2018-01-23 | 2021-09-28 | 杭州海康威视系统技术有限公司 | Storage system, and method and device for allocating storage resources |
JP7082373B2 (en) * | 2019-03-27 | 2022-06-08 | 日本電信電話株式会社 | Data management device and data management method |
US11847333B2 (en) * | 2019-07-31 | 2023-12-19 | EMC IP Holding Company, LLC | System and method for sub-block deduplication with search for identical sectors inside a candidate block |
CN110989934B (en) * | 2019-12-05 | 2023-08-25 | 达闼机器人股份有限公司 | Block chain link point data storage method, block chain system and block chain node |
US20210200717A1 (en) * | 2019-12-26 | 2021-07-01 | Oath Inc. | Generating full metadata from partial distributed metadata |
CN111427854B (en) * | 2020-03-23 | 2024-01-30 | 深圳震有科技股份有限公司 | Stack structure realizing method, device, equipment and medium for supporting storage batch data |
JP7102460B2 (en) * | 2020-05-27 | 2022-07-19 | 株式会社日立製作所 | Data management method in distributed storage device and distributed storage device |
US20220276800A1 (en) * | 2021-02-26 | 2022-09-01 | EMC IP Holding Company LLC | Mapping Storage Volumes to Storage Processing Nodes Using Input/Output Operation Constraints and Cost Function |
Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6282607B1 (en) * | 1997-09-18 | 2001-08-28 | Lucent Technologies, Inc. | Efficient scheduling of reading data from multiple storage mediums to satisfy multiple requests |
US20030033308A1 (en) * | 2001-08-03 | 2003-02-13 | Patel Sujal M. | System and methods for providing a distributed file system utilizing metadata to track information about data stored throughout the system |
US20050125456A1 (en) * | 2003-12-09 | 2005-06-09 | Junichi Hara | File migration method based on access history |
US7103740B1 (en) * | 2003-12-31 | 2006-09-05 | Veritas Operating Corporation | Backup mechanism for a multi-class file system |
US20080062870A1 (en) * | 2006-09-12 | 2008-03-13 | Foleeo, Inc. | Hive-based peer-to-peer network |
US20080162779A1 (en) * | 2006-12-29 | 2008-07-03 | John Mark Morris | Transparent data temperature sensitive cluster duplication |
US20090222509A1 (en) * | 2008-02-29 | 2009-09-03 | Chao King | System and Method for Sharing Storage Devices over a Network |
US7610319B1 (en) * | 2004-03-01 | 2009-10-27 | Symantec Operating Corporation | Efficient operations using assistance from secondary site |
US7660790B1 (en) * | 2005-02-24 | 2010-02-09 | Symantec Operating Corporation | Method and apparatus for utilizing a file change log |
US7908302B1 (en) * | 2004-09-17 | 2011-03-15 | Symantec Operating Corporation | In-place splitting and merging of files |
US8315995B1 (en) * | 2008-09-09 | 2012-11-20 | Peer Fusion, Inc. | Hybrid storage system |
US8429346B1 (en) * | 2009-12-28 | 2013-04-23 | Emc Corporation | Automated data relocation among storage tiers based on storage load |
Family Cites Families (78)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5463768A (en) | 1994-03-17 | 1995-10-31 | General Electric Company | Method and system for analyzing error logs for diagnostics |
US5778444A (en) | 1996-05-06 | 1998-07-07 | Motorola, Inc. | Method and apparatus for reset-sensitive and controlled register write accesses in a data processing system with user and test modes |
US6148368A (en) * | 1997-07-31 | 2000-11-14 | Lsi Logic Corporation | Method for accelerating disk array write operations using segmented cache memory and data logging |
US6178482B1 (en) | 1997-11-03 | 2001-01-23 | Brecis Communications | Virtual register sets |
CN100405247C (en) * | 1999-03-03 | 2008-07-23 | 索尼公司 | Data processing device, data processing method, terminal, transmission method for data processing device |
US6449671B1 (en) * | 1999-06-09 | 2002-09-10 | Ati International Srl | Method and apparatus for busing data elements |
US6598179B1 (en) | 2000-03-31 | 2003-07-22 | International Business Machines Corporation | Table-based error log analysis |
US6829678B1 (en) * | 2000-07-18 | 2004-12-07 | International Business Machines Corporation | System for determining the order and frequency in which space is allocated on individual storage devices |
JP2002207620A (en) | 2001-01-10 | 2002-07-26 | Toshiba Corp | File system and data caching method of the same system |
US7627860B2 (en) | 2001-08-14 | 2009-12-01 | National Instruments Corporation | Graphically deployment of a program with automatic conversion of program type |
US7117243B2 (en) | 2001-10-02 | 2006-10-03 | Citrix Systems, Inc. | Methods for distributed program execution with file-type association in a client-server network |
US7509645B2 (en) * | 2002-10-17 | 2009-03-24 | Intel Corporation | Methods and apparatus for load balancing storage nodes in a distributed network attached storage system |
EP1584011A4 (en) * | 2003-01-02 | 2010-10-06 | F5 Networks Inc | Metadata based file switch and switched file system |
US7055071B2 (en) | 2003-01-09 | 2006-05-30 | International Business Machines Corporation | Method and apparatus for reporting error logs in a logical environment |
US7127568B2 (en) * | 2003-01-23 | 2006-10-24 | Hitachi, Ltd. | Throttling in storage systems |
US7373451B2 (en) | 2003-12-08 | 2008-05-13 | The Board Of Trustees Of The Leland Stanford Junior University | Cache-based system management architecture with virtual appliances, network repositories, and virtual appliance transceivers |
US20050166011A1 (en) | 2004-01-23 | 2005-07-28 | Burnett Robert J. | System for consolidating disk storage space of grid computers into a single virtual disk drive |
US20070271604A1 (en) | 2004-03-17 | 2007-11-22 | Fidelitygenetic Ltd. | Secure Transaction of Dna Data |
JP4147198B2 (en) | 2004-03-23 | 2008-09-10 | 株式会社日立製作所 | Storage system |
JP4402997B2 (en) * | 2004-03-26 | 2010-01-20 | 株式会社日立製作所 | Storage device |
US7512721B1 (en) | 2004-05-25 | 2009-03-31 | Qlogic, Corporation | Method and apparatus for efficient determination of status from DMA lists |
US7650331B1 (en) | 2004-06-18 | 2010-01-19 | Google Inc. | System and method for efficient large-scale data processing |
EP1622009A1 (en) * | 2004-07-27 | 2006-02-01 | Texas Instruments Incorporated | JSM architecture and systems |
GB2418326B (en) | 2004-09-17 | 2007-04-11 | Hewlett Packard Development Co | Network vitrualization |
GB2419697A (en) | 2004-10-29 | 2006-05-03 | Hewlett Packard Development Co | Virtual overlay infrastructures each having an infrastructure controller |
GB2419702A (en) | 2004-10-29 | 2006-05-03 | Hewlett Packard Development Co | Virtual overlay infrastructures which can be suspended and later reactivated |
GB2419701A (en) | 2004-10-29 | 2006-05-03 | Hewlett Packard Development Co | Virtual overlay infrastructure with dynamic control of mapping |
US20060161753A1 (en) | 2005-01-18 | 2006-07-20 | Aschoff John G | Method, apparatus and program storage device for providing automatic performance optimization of virtualized storage allocation within a virtualized storage subsystem |
US7346734B2 (en) * | 2005-05-25 | 2008-03-18 | Microsoft Corporation | Cluster storage collection based data management |
JP4681374B2 (en) * | 2005-07-07 | 2011-05-11 | 株式会社日立製作所 | Storage management system |
US7761573B2 (en) | 2005-12-07 | 2010-07-20 | Avaya Inc. | Seamless live migration of virtual machines across optical networks |
EP1818844B1 (en) | 2006-02-10 | 2013-03-13 | Secunet Security Networks Aktiengesellschaft | Method for using security tokens |
US7814279B2 (en) * | 2006-03-23 | 2010-10-12 | International Business Machines Corporation | Low-cost cache coherency for accelerators |
US20070288921A1 (en) | 2006-06-13 | 2007-12-13 | King Steven R | Emulating a network-like communication connection between virtual machines on a physical device |
JP2008077290A (en) * | 2006-09-20 | 2008-04-03 | Hitachi Ltd | Information processor, information processing method and storage system |
US20080077635A1 (en) * | 2006-09-22 | 2008-03-27 | Digital Bazaar, Inc. | Highly Available Clustered Storage Network |
US20080086515A1 (en) | 2006-10-06 | 2008-04-10 | International Business Machines Corporation | Method and System for a Soft Error Collection of Trace Files |
US7653833B1 (en) | 2006-10-31 | 2010-01-26 | Hewlett-Packard Development Company, L.P. | Terminating a non-clustered workload in response to a failure of a system with a clustered workload |
US20080205415A1 (en) | 2007-02-28 | 2008-08-28 | Morales Henry N Jerez | Access, Connectivity and Interoperability for Devices and Services |
US7865575B2 (en) * | 2007-03-30 | 2011-01-04 | Sterling Commerce, Inc. | Methods and apparatus to perform file transfers in distributed file systems |
US20080270704A1 (en) | 2007-04-30 | 2008-10-30 | He Dingshan | Cache arrangement for improving raid i/o operations |
US8051362B2 (en) * | 2007-06-15 | 2011-11-01 | Microsoft Corporation | Distributed data storage using erasure resilient coding |
US8055864B2 (en) * | 2007-08-06 | 2011-11-08 | International Business Machines Corporation | Efficient hierarchical storage management of a file system with snapshots |
US20090097657A1 (en) | 2007-10-05 | 2009-04-16 | Scheidt Edward M | Constructive Channel Key |
JP4480756B2 (en) | 2007-12-05 | 2010-06-16 | 富士通株式会社 | Storage management device, storage system control device, storage management program, data storage system, and data storage method |
CN101470683B (en) * | 2007-12-26 | 2010-12-08 | 深圳市闪联信息技术有限公司 | Apparatus and system for copying data to multiple memory media, and copying method thereof |
JP2009205555A (en) * | 2008-02-28 | 2009-09-10 | Toshiba Corp | Memory system |
US8156491B2 (en) | 2008-02-29 | 2012-04-10 | Red Hat, Inc. | Fault tolerant virtual machine |
US20090249471A1 (en) | 2008-03-27 | 2009-10-01 | Moshe Litvin | Reversible firewall policies |
US8990911B2 (en) | 2008-03-30 | 2015-03-24 | Emc Corporation | System and method for single sign-on to resources across a network |
US8103776B2 (en) | 2008-08-29 | 2012-01-24 | Red Hat, Inc. | Systems and methods for storage allocation in provisioning of virtual machines |
US8065714B2 (en) | 2008-09-12 | 2011-11-22 | Hytrust, Inc. | Methods and systems for securely managing virtualization platform |
US8086634B2 (en) * | 2008-10-07 | 2011-12-27 | Hitachi, Ltd. | Method and apparatus for improving file access performance of distributed storage system |
CN101408855B (en) * | 2008-11-07 | 2010-06-02 | 北京威视数据系统有限公司 | Method for protecting remote backup equipment of temporary abnormity by continuous data protective system |
WO2010090664A1 (en) | 2009-02-05 | 2010-08-12 | Wwpass Corporation | Centralized authentication system with safe private data storage and method |
JP5478107B2 (en) * | 2009-04-22 | 2014-04-23 | 株式会社日立製作所 | Management server device for managing virtual storage device and virtual storage device management method |
CN102460393B (en) | 2009-05-01 | 2014-05-07 | 思杰系统有限公司 | Systems and methods for establishing a cloud bridge between virtual storage resources |
US8429647B2 (en) | 2009-05-06 | 2013-04-23 | Vmware, Inc. | Virtual machine migration across network by publishing routes to the associated virtual networks via virtual router after the start of migration of the virtual machine |
CN101639848B (en) * | 2009-06-01 | 2011-06-01 | 北京四维图新科技股份有限公司 | Spatial data engine and method applying management spatial data thereof |
US8463885B2 (en) | 2009-08-31 | 2013-06-11 | Red Hat, Inc. | Systems and methods for generating management agent installations |
US8693485B2 (en) | 2009-10-14 | 2014-04-08 | Dell Products, Lp | Virtualization aware network switch |
US8537860B2 (en) | 2009-11-03 | 2013-09-17 | International Business Machines Corporation | Apparatus for switching traffic between virtual machines |
US8949408B2 (en) | 2009-12-18 | 2015-02-03 | Microsoft Corporation | Session monitoring of virtual desktops in a virtual machine farm |
US9953178B2 (en) | 2010-02-03 | 2018-04-24 | Os Nexus, Inc. | Role based access control utilizing scoped permissions |
CA2793401C (en) | 2010-03-17 | 2019-05-07 | Siamak Farah | A cloud-based desktop and subscription application platform apparatuses, methods and systems |
US9443078B2 (en) | 2010-04-20 | 2016-09-13 | International Business Machines Corporation | Secure access to a virtual machine |
US20120185688A1 (en) | 2011-01-13 | 2012-07-19 | Google Inc. | Processor mode locking |
US8874888B1 (en) | 2011-01-13 | 2014-10-28 | Google Inc. | Managed boot in a cloud system |
US8745329B2 (en) | 2011-01-20 | 2014-06-03 | Google Inc. | Storing data across a plurality of storage nodes |
US8812586B1 (en) | 2011-02-15 | 2014-08-19 | Google Inc. | Correlating status information generated in a computer network |
US8533796B1 (en) | 2011-03-16 | 2013-09-10 | Google Inc. | Providing application programs with access to secured resources |
US8261295B1 (en) | 2011-03-16 | 2012-09-04 | Google Inc. | High-level language for specifying configurations of cloud-based deployments |
US9069616B2 (en) | 2011-09-23 | 2015-06-30 | Google Inc. | Bandwidth throttling of virtual disks |
US8276140B1 (en) | 2011-11-14 | 2012-09-25 | Google Inc. | Adjustable virtual network performance |
US8800009B1 (en) | 2011-12-30 | 2014-08-05 | Google Inc. | Virtual machine service access |
US8677449B1 (en) | 2012-03-19 | 2014-03-18 | Google Inc. | Exposing data to virtual machines |
US8909939B1 (en) | 2012-04-04 | 2014-12-09 | Google Inc. | Distribution of cryptographic host keys in a cloud computing environment |
US8813240B1 (en) | 2012-05-30 | 2014-08-19 | Google Inc. | Defensive techniques to increase computer security |
-
2011
- 2011-01-20 US US13/010,548 patent/US8745329B2/en active Active
-
2012
- 2012-01-19 AU AU2012207328A patent/AU2012207328B2/en active Active
- 2012-01-19 WO PCT/US2012/021846 patent/WO2012100037A1/en active Application Filing
- 2012-01-19 CN CN201280012751.4A patent/CN103597482B/en active Active
- 2012-01-19 CN CN201710008369.0A patent/CN106909317B/en active Active
- 2012-01-19 DE DE202012013432.9U patent/DE202012013432U1/en not_active Expired - Lifetime
- 2012-01-19 EP EP12736911.4A patent/EP2666111B1/en active Active
-
2014
- 2014-06-02 US US14/293,330 patent/US9250830B2/en active Active
-
2015
- 2015-12-09 AU AU2015268620A patent/AU2015268620B1/en active Active
-
2016
- 2016-01-19 US US15/000,718 patent/US20160132263A1/en not_active Abandoned
- 2016-04-14 AU AU2016202362A patent/AU2016202362B2/en active Active
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6282607B1 (en) * | 1997-09-18 | 2001-08-28 | Lucent Technologies, Inc. | Efficient scheduling of reading data from multiple storage mediums to satisfy multiple requests |
US20030033308A1 (en) * | 2001-08-03 | 2003-02-13 | Patel Sujal M. | System and methods for providing a distributed file system utilizing metadata to track information about data stored throughout the system |
US20050125456A1 (en) * | 2003-12-09 | 2005-06-09 | Junichi Hara | File migration method based on access history |
US7103740B1 (en) * | 2003-12-31 | 2006-09-05 | Veritas Operating Corporation | Backup mechanism for a multi-class file system |
US7610319B1 (en) * | 2004-03-01 | 2009-10-27 | Symantec Operating Corporation | Efficient operations using assistance from secondary site |
US7908302B1 (en) * | 2004-09-17 | 2011-03-15 | Symantec Operating Corporation | In-place splitting and merging of files |
US7660790B1 (en) * | 2005-02-24 | 2010-02-09 | Symantec Operating Corporation | Method and apparatus for utilizing a file change log |
US20080062870A1 (en) * | 2006-09-12 | 2008-03-13 | Foleeo, Inc. | Hive-based peer-to-peer network |
US20080162779A1 (en) * | 2006-12-29 | 2008-07-03 | John Mark Morris | Transparent data temperature sensitive cluster duplication |
US20090222509A1 (en) * | 2008-02-29 | 2009-09-03 | Chao King | System and Method for Sharing Storage Devices over a Network |
US8315995B1 (en) * | 2008-09-09 | 2012-11-20 | Peer Fusion, Inc. | Hybrid storage system |
US8429346B1 (en) * | 2009-12-28 | 2013-04-23 | Emc Corporation | Automated data relocation among storage tiers based on storage load |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10713276B2 (en) * | 2016-10-03 | 2020-07-14 | Ocient, Inc. | Data transition in highly parallel database management system |
US11294932B2 (en) | 2016-10-03 | 2022-04-05 | Ocient Inc. | Data transition in highly parallel database management system |
US11934423B2 (en) | 2016-10-03 | 2024-03-19 | Ocient Inc. | Data transition in highly parallel database management system |
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