Progressive workload migration recommendation based on microstep score

Patent metadata

Field	Value
Publication number	US-12061926-B2
Application number	US-202117446374-A
Country	US
Kind code	B2
Filing date	Aug 30, 2021
Priority date	Aug 30, 2021
Publication date	Aug 13, 2024
Grant date	Aug 13, 2024

Abstract

One example method includes discovering computing workloads that are available to migrate from a current platform to a target platform, and the workloads are controlled by a user, determining that the computing workloads are migratable from the current platform to the target platform, ordering the computing workloads according to a respective measurable aspect, such as SLA (Service Level Agreement) for example, of each of the computing workloads, and generating a recommendation to the user that one of the computing workloads be migrated to the target platform, and the recommendation is generated based on a microstep score that has been assigned to the user.

First claim

What is claimed is:

1. A method, comprising:
discovering computing workloads that are available to migrate from a current platform to a target platform, wherein the workloads are controlled by a user;
determining that the computing workloads are migratable from the current platform to the target platform;
ordering the computing workloads according to a respective measurable aspect of each of the computing workloads;
generating a recommendation that one of the computing workloads be migrated to the target platform, wherein the recommendation is generated based on a microstep score that has been assigned to the user; and
migrating, when the user accepts the recommendation, the computing workload identified in the recommendation to the target platform,
wherein, when the microstep score is greater than a threshold, all of the computing workloads are recommended for migration regardless of respective Service Level Agreements (SLAs) associated with the computing workloads.

2. The method as recited in claim 1 , wherein the microstep score is increased when the user accepts the recommendation, and the microstep score is decreased when the user rejects the recommendation.

3. The method as recited in claim 1 , wherein ordering the computing workloads according to the respective measurable aspect of each of the computing workloads comprises ordering the workloads from a lowest SLA (Service Level Agreement) to highest SLA.

4. The method as recited in claim 1 , wherein the recommendation is generated based on the measurable aspect of the computing workload that was recommended for migration.

5. The method as recited in claim 1 , determining that the computing workloads are migratable from the current platform to the target platform comprises applying a filter to the computing workloads available for migration to filter out computing workloads that are not available for migration.

6. The method as recited in claim 5 , further comprising applying a secondary filter to the migratable computing workloads.

7. The method as recited in claim 1 , further comprising assigning the microstep score to the user.

8. The method as recited in claim 1 , wherein the current platform is a hyperscaler environment.

9. The method as recited in claim 1 , further comprising retaining a history of the computer workloads, recommendation, and microstep score, and using the history as a basis for a future recommendation.

10. A computer readable storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
discovering computing workloads that are available to migrate from a current platform to a target platform, wherein the workloads are controlled by a user;
determining that the computing workloads are migratable from the current platform to the target platform;
ordering the computing workloads according to a respective SLA (Service Level Agreement) of each of the computing workloads;
generating a recommendation to the user that one of the computing workloads be migrated to the target platform, wherein the recommendation is generated based on a microstep score that has been assigned to the user; and
migrating, when the user accepts the recommendation, the computing workload identified in the recommendation to the target platform,
wherein, when the microstep score is greater than a threshold, all of the computing workloads are recommended for migration regardless of respective Service Level Agreements (SLAs) associated with the computing workloads.

11. The computer readable storage medium as recited in claim 10 , wherein the microstep score is increased when the user accepts the recommendation, and the microstep score is decreased when the user rejects the recommendation.

12. The computer readable storage medium as recited in claim 10 , wherein ordering the computing workloads according to the respective SLA of each of the computing workloads comprises ordering the workloads from lowest SLA to highest SLA.

13. The computer readable storage medium as recited in claim 10 , wherein the recommendation is generated based on the SLA of the computing workload that was recommended for migration.

14. The computer readable storage medium as recited in claim 10 , determining that the computing workloads are migratable from the current platform to the target platform comprises applying a filter to the computing workloads available for migration to filter out computing workloads that are not available for migration.

15. The computer readable storage medium as recited in claim 14 , wherein the operations further comprise applying a secondary filter to the migratable computing workloads.

16. The computer readable storage medium as recited in claim 10 , wherein the operations further comprise assigning the microstep score to the user.

17. The computer readable storage medium as recited in claim 11 , wherein the current platform is a hyperscaler environment.

18. The computer readable storage medium as recited in claim 10 , wherein the operations further comprise retaining a history of the computer workloads, recommendation, and microstep score, and using the history as a basis for a future recommendation.

Assignees

• Emc Ip Holding Co Llc

Inventors

• Reineke Nicole
• Schulze Corinne

Classifications

• G06F9/5083

  Techniques for rebalancing the load in a distributed system · CPC title

• G06F9/5088

  involving task migration · CPC title

• G06F9/4875 Primary

  with migration policy, e.g. auction, contract negotiation · CPC title