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Autonomically defining hot storage and heavy workloads

A technology for defining and storing hierarchies, applied to instruments, hierarchical databases, relational databases, etc.

Active Publication Date: 2015-03-18
IBM CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the above approach requires user intervention to determine which data or workloads should get proportionally more access to fast and expensive resources and which should be attributed more access to slower and cheaper resources

Method used

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  • Autonomically defining hot storage and heavy workloads
  • Autonomically defining hot storage and heavy workloads
  • Autonomically defining hot storage and heavy workloads

Examples

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Embodiment Construction

[0008] Those skilled in the art know that various aspects of the present invention can be implemented as a system, method or computer program product. Therefore, various aspects of the present invention can be embodied in the following forms, that is: a complete hardware implementation, a complete software implementation (including firmware, resident software, microcode, etc.), or a combination of hardware and software implementations, These may collectively be referred to herein as "circuits," "modules," or "systems." Furthermore, various aspects of the present invention can also be implemented in the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied therein.

[0009] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for...

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Abstract

In defining database objects for storage in a storage hierarchy, frequencies of accesses of a plurality of database objects over a predetermined time period are observed. A mean and standard deviation for the plurality of database objects are computed based on the observed frequencies of accesses of the plurality of database objects. A z-score for a given database object is determined based on a comparison of the frequency of access for the given database with the mean and standard deviation computed for the plurality of database objects by the computing processor; and a level in the storage hierarchy corresponding to the z-score of the given database object is determined. The given database object may then be stored at the level in the storage hierarchy corresponding to the z-score of the given database object.

Description

Background technique [0001] Different types of data storage incur different costs, with faster storage costing more per gigabyte than slower storage. One way to manage storage costs is to use tiering or "multi-temperature storage," where frequently accessed (aka "hot") data is stored on faster, but more expensive storage devices such as solid-state "disk ”), while infrequently accessed data is moved to slower, but cheaper storage devices (e.g., physical hard disks, tape drives, etc.). [0002] Specific database applications or users also exhibit different workload patterns. Some have high frequency access to data, while others may have less frequent data access. In another approach, workload management techniques perform similar tasks to multi-temperature storage, providing a mechanism to give different workloads different levels of resource access. For example, giving high-priority workloads a larger share of available CPU time than other workloads is qualitatively similar...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F17/30589G06F17/30595G06F17/30312G06F16/22G06F16/282G06F16/217G06F3/0604G06F3/0613G06F3/0653G06F3/0671G06F3/0683
Inventor K·L·贝克K·J·奇考尔
Owner IBM CORP