Method, electronic device, and computer program product for scheduling data collection

Patent metadata

Field	Value
Publication number	US-12019869-B2
Application number	US-202217978716-A
Country	US
Kind code	B2
Filing date	Nov 1, 2022
Priority date	Oct 12, 2022
Publication date	Jun 25, 2024
Grant date	Jun 25, 2024

Abstract

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for scheduling data collection. The method includes acquiring a plurality of running parameters of a storage system. The method further includes determining a plurality of grade ranges of each of the plurality of running parameters, the grade ranges indicating degrees of impact on scheduling for data collection. The method further includes determining a plurality of relevancies of the plurality of running parameters for the plurality of grade ranges. The method further includes determining scheduling for the data collection based on the plurality of relevancies. The method can dynamically determine when to perform data collection, thus avoiding data loss.

First claim

What is claimed is:

1. A method for scheduling data collection, comprising:
acquiring a plurality of running parameters of a storage system;
determining a plurality of grade ranges of each of the plurality of running parameters, the grade ranges indicating degrees of impact on scheduling for the data collection;
determining a plurality of relevancies of the plurality of running parameters for the plurality of grade ranges; and
determining scheduling for the data collection based on the plurality of relevancies;
wherein determining scheduling for the data collection comprises:
determining one or more relevant grade ranges for each running parameter according to the plurality of relevancies between each of the plurality of running parameters and the plurality of grade ranges;
determining one or more candidate adjustment schemes based on the one or more relevant grade ranges for each running parameter; and
determining a target adjustment scheme from the one or more candidate adjustment schemes based on the plurality of relevancies.

2. The method according to claim 1 , wherein acquiring the plurality of running parameters of the storage system comprises:
acquiring at least two of the following running parameters:
a health parameter, indicating the number or degree of problems in the storage system;
a pressure parameter, indicating a usage rate of computing resources of the storage system;
a configuration parameter, indicating a usage rate of storage resources of the storage system; and
a time interval parameter, indicating a time interval from previous data collection.

3. The method according to claim 1 , wherein determining the plurality of relevancies comprises:
acquiring a parameter distribution of each of the plurality of grade ranges; and
determining the relevancies of the running parameters for each grade range based on the running parameters and the parameter distribution.

4. The method according to claim 1 , wherein determining the target adjustment scheme comprises:
determining a score of each candidate adjustment scheme based on the relevancy of the relevant grade range corresponding to each of the one or more candidate adjustment schemes; and
determining the candidate adjustment scheme with the highest score as the target adjustment scheme.

5. The method according to claim 4 , wherein determining the score of the candidate adjustment scheme comprises:
determining weights of the plurality of relevancies for each of the plurality of running parameters based on degrees of impact of the plurality of running parameters on the scheduling of the data collection; and
determining a weighted sum of the relevancies of the relevant grade ranges corresponding to each of the one or more candidate adjustment schemes as the score.

6. The method according to claim 1 , wherein at least two of the plurality of grade ranges of each running parameter partially overlap.

7. A method for scheduling data collection, comprising:
acquiring a plurality of running parameters of a storage system;
determining a plurality of grade ranges of each of the plurality of running parameters, the grade ranges indicating degrees of impact on scheduling for the data collection;
determining a plurality of relevancies of the plurality of running parameters for the plurality of grade ranges; and
determining scheduling for the data collection based on the plurality of relevancies;
wherein determining scheduling for the data collection comprises:
acquiring a decision tree, a root node of the decision tree and each layer of internal nodes corresponding to one or more grade ranges of one running parameter, and one leaf node of the decision tree corresponding to one adjustment scheme; and
determining a target adjustment scheme based on the decision tree and the relevancies.

8. An electronic device, comprising:
at least one processor; and
a memory coupled to the at least one processor, wherein the memory has instructions stored therein which, when executed by the at least one processor, cause the electronic device to execute actions comprising:
acquiring a plurality of running parameters of a storage system;
determining a plurality of grade ranges of each of the plurality of running parameters, the grade ranges indicating degrees of impact on scheduling for data collection;
determining a plurality of relevancies of the plurality of running parameters for the plurality of grade ranges; and
determining scheduling for the data collection based on the plurality of relevancies;
wherein determining scheduling for the data collection comprises:
determining one or more relevant grade ranges for each running parameter according to the plurality of relevancies between each of the plurality of running parameters and the plurality of grade ranges;
determining one or more candidate adjustment schemes based on the one or more relevant grade ranges for each running parameter; and
determining a target adjustment scheme from the one or more candidate adjustment schemes based on the plurality of relevancies.

9. The electronic device according to claim 8 , wherein acquiring the plurality of running parameters of the storage system comprises:
acquiring at least two of the following running parameters:
a health parameter, indicating the number or degree of problems in the storage system;
a pressure parameter, indicating a usage rate of computing resources of the storage system;
a configuration parameter, indicating a usage rate of storage resources of the storage system; and
a time interval parameter, indicating a time interval from previous data collection.

10. The electronic device according to claim 8 , wherein determining the plurality of relevancies comprises:
acquiring a parameter distribution of each of the plurality of grade ranges; and
determining the relevancies of the running parameters for each grade range based on the running parameters and the parameter distribution.

11. The electronic device according to claim 8 , wherein determining the one or more candidate adjustment schemes comprises:
acquiring a decision tree, a root node of the decision tree and each layer of internal nodes corresponding to one or more grade ranges of one running parameter, and one leaf node of the decision tree corresponding to one adjustment scheme; and
determining the one or more candidate adjustment schemes based on the decision tree and the relevancies.

12. The electronic device according to claim 8 , wherein determining the target adjustment scheme comprises:
determining a score of each candidate adjustment scheme based on the relevancy of the relevant grade range corresponding to each of the one or more candidate adjustment schemes; and
determining the candidate adjustment scheme with the highest score as the target adjustment scheme.

13. The electronic device according to claim 12 , wherein determining the score of the candidate adjustment scheme comprises:
determining weights of the plurality of relevancies for each of the plurality of running parameters based on degrees of impact of the plurality of running parameters on the scheduling of the data collection; and
determining a weighted sum of the relevancies of the relevant grade ranges corresponding to each of the one or more candidate adjustment schemes as the score.

14. The electronic device according to claim 8 , wherein at least two of the plurality of grade ranges of each running parameter partially overlap.

15. A computer program product tangibly stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherei

Assignees

• Dell Products Lp

Inventors

• Du Fang
• Pu Weilan
• Chen Peilei

Classifications

• G06F11/3072

  where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting · CPC title

• G06F11/3452

  Performance evaluation by statistical analysis · CPC title

• G06F11/3055

  Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available (error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction of the data by redundancy in operations G06F11/14; error detection or correction by redundancy in hardware G06F11/16) · CPC title

• G06F11/3034

  where the computing system component is a storage system, e.g. DASD based or network based (digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097) · CPC title

• G06F11/0766

  Error or fault reporting or storing · CPC title