SYSTEM AND METHOD FOR CAPACITY PLANNING FOR COMPUTING SYSTEMS

• US 20080221941A1
• Filed: 03/09/2007
• Published: 09/11/2008
• Est. Priority Date: 03/09/2007
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Zhang, Qi, Cherkasova, Ludmila, Mathews, Guy, Greene, Wayne
• Granted Patent: US 9,135,075 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3414 Workload generation, e.g. scripts, playback
• CPC Class Codes: G06F 11/3423 where the assessed time is active or idle time
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 2201/87 Monitoring of transactions
• CPC Class Codes: G06F 2209/508 Monitor
• CPC Class Codes: G06F 9/505 considering the load

Abstract

A method comprises receiving, by a workload profiler, a representative workload of a computing system under analysis. The workload profiler determines a workload profile of the computing system that reflects a transaction mix that varies over times. A capacity analyzer receives the workload profile, and determines a maximum capacity of the computing system under analysis for serving the workload profile while satisfying a defined quality of service (QoS) target.

Claims

1. A method comprising:

receiving, by a workload profiler, a representative workload of a computing system under analysis;

determining, by the workload profiler, a workload profile of the computing system that reflects a transaction mix that varies over time;

receiving, by a capacity analyzer, the determined workload profile; and

determining, by the capacity analyzer, a maximum capacity of the computing system under analysis for serving the workload profile while satisfying a defined quality of service (QoS) target.

2. The method of claim 1 wherein the workload profile comprises, for each of a plurality of monitoring windows, a plurality of composite transactions;

where a mix of the plurality of composite transactions varies across the plurality of monitoring windows.

3. The method of claim 1 wherein the workload profile reflects each of a plurality of composite transaction types present in the representative workload as a fraction of overall transaction types present in the representative workload.

4. The method of claim 1 further comprising:

determining, by the workload profiler, at least one session in the representative workload, wherein the session comprises a plurality of related composite transactions for providing a service to a client;

determining, by the workload profiler, a think time value reflecting idle time of the computing system between the plurality of composite transactions for the at least one session;

receiving by the capacity analyzer, the determined think time value; and

determining the maximum capacity, by the capacity analyzer, based at least in part on the determined think time value.

5. The method of claim 4 further comprising:

determining, by a resource cost calculator, a resource cost for at least one composite transaction in the representative workload, said resource cost reflecting an amount of utilization of at least one resource of the computing system in serving the at least one composite transaction;

receiving, by the capacity analyzer, the determined resource cost for the at least one composite transaction; and

wherein said determining, by the capacity analyzer, the maximum capacity is further based at least in part on the determined resource cost for the at least one composite transaction.

6. The method of claim 5 wherein the determining the resource cost for the at least one composite transaction comprises:

performing, by said resource cost calculator, a statistical regression-based analysis.

7. The method of claim 6 wherein said statistical regression-based analysis comprises;

determining, an approximate utilization (U′

_CPU,n) as;

8. The method of claim 1 further comprising:

determining, by the workload profiler, a subset of composite transactions included in the received representative workload to include in the workload profile.

9. The method of claim 8 wherein said determining the subset comprises:

determining K most popular composite transaction types in the representative workload, wherein K is a number that is at least 1 and that is less than a total number of composite transaction types included in the representative workload.

10. The method of claim 9 further comprising:

determining, by a resource cost calculator, resource cost for composite transactions included in the representative workload that are of the determined K most popular composite transaction types, said resource cost reflecting an amount of utilization of at least one resource of the computing system in serving the at least one composite transaction.

11. The method of claim 1 wherein said workload profiler and said capacity analyzer comprise computer-executable software code stored to a computer-readable medium.

12. The method of claim 1 wherein said computing system hosts at least one multiplier applications said determining said maximum capacity of the computing system comprises:

determining, by the capacity analyzers said maximum capacity of the computing system under analysis for each of a plurality of different tiers of the at least one multi-tier application.

13. The method of claim 12 wherein said capacity analyzer employs an analytical model based on a network of queues representing different tiers of the multi-tier application.

14. The method of claim. 1 wherein said determining said maximum capacity of the computing system under analysis comprises:

determining a maximum number of clients that can be served by the computing system under analysis while satisfying said defined QoS target.

15. The method of claim 14 wherein said maximum number of clients that the computing system can support comprises a maximum number of clients that the computing system can serve within a defined time interval in compliance with said defined QoS target.

16. The method of claim 15 wherein said maximum number of clients comprises a maximum number of clients that the computing system can serve concurrently in compliance with said defined QoS target.

17. The method of claim 1 wherein said defined QoS target comprises a defined maximum response time for said computing system to respond to a client request.

18. A system comprising:

workload profiler operable to receive a representative workload of a computing system, said representative workload comprising a plurality of composite transactions;

said workload profiler further operable to determine a think time value for at least one session in the representative workload, said at least one session comprising a plurality of related composite transactions for providing a service to a client, and said think time value reflecting idle time of the computing system between the plurality of composite transactions in the at least one session;

resource cost calculator operable to determine resource cost for at least one composite transaction in the representative workload, said resource cost reflecting an amount of utilization of at least one resource of the computing system in serving the at least one composite transaction; and

capacity analyzer operable to receive the determined think time value for the at least one session and the determined resource cost for the at least one composite transactions and determine a maximum number of clients supportable by the computing system under analysis while satisfying a defined quality of service (QoS) target.

19. The system of claim 18 wherein a mix of the plurality of composite transactions vary over a plurality of monitoring windows.

20. The system of claim 18 further comprising:

said workload profiler further operable to determine a subset of the plurality of composite transactions in the representative workload, said subset comprising K most popular composite transaction types in the representative workload, wherein K is a number that is at least 1 and that is less than a total number of composite transaction types included in the representative workload; and

said at least one composite transaction for which said resource cost calculator determines said resource cost comprises composite transactions included in said determined subset.

21. The system of claim 18 wherein said resource cost calculator comprises a regression-based solver operable to perform a statistical regression-based analysis for determining said resource cost.

22. The system of claim 18 wherein said computing system hosts at least one multi-tier application, and wherein said capacity analyzer is operable to determine said maximum number of clients for each of a plurality of different tiers of the at least one multi-tier application.

23. The system of claim 22 wherein said capacity analyzer employs an analytical model based on a network of queues representing different tiers of the multi-tier application.

24. A method comprising:

receiving, by a workload profiler, a representative workload of a computing system, said representative workload comprising a plurality of composite transactions;

determining, by the workload profiler, a subset of the plurality of composite transactions for which a corresponding resource cost is to be determined, said subset comprising at least one of the plurality of composite transactions;

determining, by the workload profiler, a think time value for at least one session;

determining, by a regression-based solver, a resource cost for the at least one composite transaction in the determined subset, said resource cost reflecting an amount of utilization of at least one resource of the computing system in serving the at least one composite transaction in the determined subset; and

determining by a capacity analyzer, based at least in part on the determined resource cost for the at least one composite transaction and the determined think time value for the at least one session, capacity of the computing system.

25. The method of claim 24 wherein the subset of the plurality of composite transactions comprises, for each of a plurality of monitoring windows, a plurality of composite transactions;

where a mix of the plurality of composite transactions varies across the plurality of monitoring windows.

26. The method of claim 24 wherein said determining said capacity of the computing system comprises:

determining a maximum capacity of the computing system for serving a workload while satisfying a defined quality of service (QoS) target.

27. The method of claim 26 wherein said determining said maximum capacity of the computing system comprises:

determining a maximum number of clients that can be served by the computing system while satisfying said defined QoS target.