Cost effective service level agreement data management

• US 10,445,253 B2
• Filed: 04/20/2016
• Issued: 10/15/2019
• Est. Priority Date: 04/20/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Alatorre, Gabriel, Ludwig, Heiko, Mandagere, Nagapramod S., Mohamed, Mohamed
• Primary Examiner(s): Khan, Masud K
• Application Number:
  US15/133,734
• Publication Number:
  US 20170308482A1
• Time in Patent Office: 1,273 Days
• CPC Class Codes: G06F 12/126 with special data handling, e.g. priority of data or instructions, handling errors or pinning
• CPC Class Codes: G06F 2212/1021 Hit rate improvement
• CPC Class Codes: G06F 2212/281 Single cache

Abstract

The embodiments described herein relate to dynamically managing metric data of a network environment with respect to a data storage system. A data retention policy is analyzed, which includes extracting one or more metric definitions from the retention policy. A relevance of a set of metric data is identified based on the analysis. The set of metric data includes an aggregation of one or more metric observations. A storage location in a data storage system for the set of metric values is selected based on the identified relevance. The data storage system includes a cache storage location and a persistent storage location. The set of metric data is retained in the selected storage location. As the retention policy is modified, select data may be re-classified and moved within the storage system based on the re-classification.

Claims

1. A system comprising:

a processor in communication with memory;

a data storage system in communication with the processor, the data storage system comprising a cache storage location and a persistent storage location; and

one or more tools in communication with the processor, the tools to;

analyze a data retention policy;

identify a relevance of a set of metric data based on the analysis, wherein the set of metric data includes an aggregation of metric observations, the identification of relevance including the one or more tools to identify the metric observations in support of the aggregation as active metric data, wherein the identified relevance is at least partially based on time;

select a storage location in the data storage system for the set of metric data based on the identified relevance; and

retain the set of metric data in the selected storage location, including the one or more tools to retain the active metric data in the cache and inactive metric data in the persistent storage; and

identifying a storage device holding data associated with a change in the data retention policy, and communicating a modified relevance between each storage device associated with the modified retention policy to mitigate a miss in data movement associated with the change in the data retention policy.

2. The system of claim 1, wherein the data storage system is a tiered storage system, wherein the cache storage is associated with a first tier, and wherein the persistent storage is associated with a second tier.

3. The system of claim 1, wherein the metric data comprises time series data as a sequence of numerical data points in successive order, and further comprising the tools to aggregate the metric data on a time period selected from the group consisting of:

daily, weekly, monthly, annually, and combinations thereof.

4. The system of claim 1, further comprising the tools to manage storage of the metric data in the storage hierarchy responsive to a status of the data in relation to the retention policy, wherein managing storage of the data comprises the tools to:

identify a status change of data activity in the storage system based on the retention policy;

move relevant data from the cache to the persistent storage in response to an identification of a status change from active to inactive; and

move relevant data from the persistent storage to the cache in response to an identification of a status change from inactive to active.

5. The system of claim 1, further comprising the tools to automate data storage management with respect to data retention policy management, including the tools to:

analyze a metric expression associated with the data retention policy, including the tools to derive a set of input metrics for each metric definition of the data retention policy;

identify a length of an input window for each metric of the derived set;

create a metric dependency graph based on the identified lengths and the derived set; and

modify the data retention policy based on the graph.

6. The system of claim 1, further comprising the tools to:

analyze a second data retention policy;

identify a cross-over of tracking data with each of the data retention policies based on the analysis; and

adjust management and movement of data in the data storage hierarchy based on the cross-over of tracking data.

7. A computer program product comprising a computer-readable storage medium having computer-readable program code embodied therewith, the program code executable by a processor to:

analyze a data retention policy;

identify a relevance of a set of metric data based on the analysis, wherein the set of metric data includes an aggregation of metric observations, the identification of relevance including the program code to identify the metric observations in support of the aggregation as active metric data, wherein the identified relevance is at least partially based on time;

select a storage location in a data storage system for the set of metric data based on the identified relevance, the data storage system comprising a cache storage location and a persistent storage location; and

retain the set of metric data in the selected storage location, including program code to retain the active metric data in the cache and inactive metric data in the persistent storage; and

identifying a storage device holding data associated with a change in the data retention policy, and communicating a modified relevance between each storage device associated with the modified retention policy to mitigate a miss in data movement associated with the change in the data retention policy.

8. The computer program product of claim 7, wherein the data storage system is a tiered storage system, wherein the cache storage is associated with a first tier, and wherein the persistent storage is associated with a second tier.

9. The computer program product of claim 7, wherein the metric data comprises time series data as a sequence of numerical data points in successive order, and further comprising program code to aggregate the metric data on a time period selected from the group consisting of:

daily, weekly, monthly, annually, and combinations thereof.

10. The computer program product of claim 7, further comprising program code to manage storage of the metric data in the storage hierarchy responsive to a status of the data in relation to the retention policy.

11. The computer program product of claim 10, further comprising program code to identify a status change of data activity in the storage system based on the retention policy, move relevant data from the cache to the persistent storage in response to an identification of a status change from active to inactive, and move relevant data from the persistent storage to the cache in response to an identification of a status change from inactive to active.

12. The computer program product of claim 7, further comprising program code to automate data storage management with respect data retention policy management, including program code to:

analyze a metric expression associated with the data retention policy, including program code to derive a set of input metrics for each metric definition of the data retention policy;

identify a length of an input window for each metric of the derived set;

create a metric dependency graph based on the identified lengths and the derived set; and

modify the data retention policy based on the graph.

13. The computer program product of claim 7, further comprising program code to:

analyze a second data retention policy;

identify a cross-over of tracking data with each of the data retention policies based on the analysis; and

adjust management and movement of data in the data storage hierarchy based on the cross-over of tracking data.

14. A method comprising:

analyzing a data retention policy;

identifying a relevance of a set of metric data based on the analysis, wherein the set of metric data includes an aggregation of metric observations, the identification of relevance including identifying the metric observations in support of the aggregation as active metric data, wherein the identified relevance is at least partially based on time;

selecting a storage location in a data storage system for the set of metric data based on the identified relevance, the data storage system comprising a cache storage location and a persistent storage location; and

retaining the set of metric data in the selected storage location, including retaining the active metric data in the cache and inactive metric data in the persistent storage; and

identifying a storage device holding data associated with a change in the data retention policy, and communicating a modified relevance between each storage device associated with the modified retention policy to mitigate a miss in data movement associated with the change in the data retention policy.

15. The method of claim 14, wherein the data storage system is a tiered storage system, wherein the cache storage is associated with a first tier, and wherein the persistent storage is associated with a second tier.

16. The method of claim 14, wherein the metric data comprises time series data as a sequence of numerical data points in successive order, and further comprising aggregating the metric data on a time period selected from the group consisting of:

daily, weekly, monthly, annually, and combinations thereof.

17. The method of claim 14, further comprising managing storage of the metric data in the storage hierarchy responsive to a status of the data in relation to the retention policy.

18. The method of claim 17, further comprising identifying a status change of data activity in the storage system based on the retention policy, moving relevant data from the cache to the persistent storage in response to identifying a status change from active to inactive, and moving relevant data from the persistent storage to the cache in response to identifying a status change from inactive to active.

19. The method of claim 14, further comprising automating data storage management with respect data retention policy management, including:

analyzing a metric expression associated with the data retention policy, including deriving a set of input metrics for each metric definition of the data retention policy;

identifying a length of an input window for each metric of the derived set;

creating a metric dependency graph based on the identified lengths and the derived set; and

modifying the data retention policy based on the graph.

20. The method of claim 14, further comprising:

analyzing a second data retention policy;

identifying a cross-over of tracking data with each of the data retention policies based on the analysis; and

adjusting management and movement of data in the data storage hierarchy based on the cross-over of tracking data.