Cloud and datacenter migration wave planning using analytics and flow modeling

Patent metadata

Field	Value
Publication number	US-10936361-B2
Application number	US-201816009177-A
Country	US
Kind code	B2
Filing date	Jun 14, 2018
Priority date	Jun 14, 2018
Publication date	Mar 2, 2021
Grant date	Mar 2, 2021

Abstract

A set of workloads to be migrated is identified. A constraint on optimization of the set of workloads is determined. A subset of workloads to be migrated is selected from the set of workloads. A model is constructed, using traffic information corresponding to the set of workloads. The model includes a representation of a relationship between a first workload and a second workload in the subset of workloads. The model is solved to cause generation of a set of optimal flow values. A schedule for a migration wave is constructed. The schedule complies with the constraint on optimization of the set of workloads.

First claim

What is claimed is:

1. A computer-implemented method comprising:
identifying a set of workloads to be migrated;
determining a constraint on optimization of the set of workloads;
selecting, from the set of workloads, a subset of workloads to be migrated;
constructing, using traffic information of network traffic among the set of workloads, a model, wherein the model comprises a representation of a traffic transfer relationship between a first workload and a second workload in the subset of workloads;
solving the model to cause generation of a set of optimal flow values; and
constructing, using the set of optimal flow values, a schedule for a migration wave, wherein the schedule complies with the constraint on optimization of the set of workloads,
wherein the traffic information of network traffic among the set of workloads comprises information regarding the amount, type, and timing of network traffic between applications running on the set of workloads to be migrated.

2. The computer-implemented method of claim 1 , further comprising determining architectural affinities between the set of workloads to be migrated based on the traffic information of network traffic among the set of workloads.

3. The computer-implemented method of claim 2 , wherein selecting a subset of workloads to be migrated is based on the determined architectural affinities.

4. The computer-implemented method of claim 1 , wherein the model comprises a Bayesian acyclic graph.

5. The computer-implemented method of claim 4 , further comprising extending the Bayesian acyclic graph to form a maximum flow model.

6. The computer-implemented method of claim 5 , wherein solving the model to cause generation of a set of optimal flow values further comprises solving the maximum flow model.

7. A computer usable program product comprising one or more computer-readable storage media, and program instructions stored on at least one of the one or more storage media, the stored program instructions comprising:
program instructions to identify a set of workloads to be migrated;
program instructions to determine a constraint on optimization of the set of workloads;
program instructions to select, from the set of workloads, a subset of workloads to be migrated;
program instructions to construct, using traffic information of network traffic among the set of workloads, a model, wherein the model comprises a representation of a traffic transfer relationship between a first workload and a second workload in the subset of workloads;
program instructions to solve the model to cause generation of a set of optimal flow values; and
program instructions to construct, using the set of optimal flow values, a schedule for a migration wave, wherein the schedule complies with the constraint on optimization of the set of workloads,
wherein the traffic information of network traffic among the set of workloads comprises information regarding the amount, type, and timing of network traffic between applications running on the set of workloads to be migrated.

8. The computer usable program product of claim 7 , further comprising program instructions to determine architectural affinities between the set of workloads to be migrated based on the traffic information of network traffic among the set of workloads.

9. The computer usable program product of claim 8 , wherein selecting a subset of workloads to be migrated is based on the determined architectural affinities.

10. The computer usable program product of claim 7 , wherein the model comprises a Bayesian acyclic graph.

11. The computer usable program product of claim 10 , further comprising program instructions to extend the Bayesian acyclic graph to form a maximum flow model.

12. The computer usable program product of claim 11 , wherein solving the model to cause generation of a set of optimal flow values further comprises solving the maximum flow model.

13. The computer usable program product of claim 7 , wherein the computer usable code is stored in a computer readable storage device in a data processing system, and wherein the computer usable code is transferred over a network from a remote data processing system.

14. The computer usable program product of claim 7 , wherein the computer usable code is stored in a computer readable storage device in a server data processing system, and wherein the computer usable code is downloaded over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system.

15. A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:
program instructions to identify a set of workloads to be migrated;
program instructions to determine a constraint on optimization of the set of workloads;
program instructions to select, from the set of workloads, a subset of workloads to be migrated;
program instructions to construct, using traffic information of network traffic among the set of workloads, a model, wherein the model comprises a representation of a traffic transfer relationship between a first workload and a second workload in the subset of workloads;
program instructions to solve the model to cause generation of a set of optimal flow values; and
program instructions to construct, using the set of optimal flow values, a schedule for a migration wave, wherein the schedule complies with the constraint on optimization of the set of workloads,
wherein the traffic information of network traffic among the set of workloads comprises information regarding the amount, type, and timing of network traffic between applications running on the set of workloads to be migrated.

16. The computer system of claim 15 , wherein the model comprises a Bayesian acyclic graph, and further comprising program instructions to extend the Bayesian acyclic graph to form a maximum flow model.

17. The computer system of claim 16 , wherein solving the model to cause generation of a set of optimal flow values further comprises solving the maximum flow model.

Assignees

• IBM

Inventors

• Nanduru Laxmikantha Sai
• Hunt Anthony M
• Zolotow Clea Anne
• Graf Florian
• Fornash Sean Thomas

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• H04L67/60

  Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources (admission control or resource allocation H04L47/70) · CPC title

• G06F9/45558 Primary

  Hypervisor-specific management and integration aspects · CPC title

• G06F9/5038

  considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration (scheduling strategies G06F9/4881 and subgroups) · CPC title

• G06N20/00

  Machine learning · CPC title