Method and apparatus providing centralized analysis of distributed system performance metrics

• US 20040236757A1
• Filed: 05/20/2003
• Published: 11/25/2004
• Est. Priority Date: 05/20/2003
• Status: Abandoned Application

• Current Assignee: EMC Corporation (Dell Technologies Inc.)
• Original Assignee: EMC Corporation (Dell Technologies Inc.)
• Inventors: Spear, Skye W., Caccavale, Frank S., Villapakkam, Sridhar C.
• Application Number:
  US10/441,866
• Publication Number:
  US 20040236757A1
• Time in Patent Office: Days
• US Class Current: 707/100
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3495 for systems

Abstract

Performance parameters are accumulated on distributed processing units and analyzed in an analysis engine. The parameters include response time measurements and workload across intervals of time. The parameters are stored in a standard instrumentation database for each processing unit. The analysis engine accesses the distributed databases over a standard interconnect network. The analysis engine uses the parameters to determine metric entropy, response time, and utilization. The analysis engine triggers an alarm if maximum limit values are exceeded, and estimates additional processing resources needed to alleviate bottlenecks and optimize system performance.

Claims

1. In a data processing network including distributed processing units, a method of analysis of system performance comprising:

each of the distributed processing units accumulating performance parameters including response time measurements and workload across intervals of time, said each of the distributed processing units storing the performance parameters accumulated by said each of the distributed processing units in an industry standard database in said each of the distributed processing units; and

accessing the industry standard databases over the data processing network to retrieve the performance parameters accumulated by the distributed processing units, and determining a measure of system performance from the retrieved performance parameters.

2. The method as claimed in claim 1, which includes triggering an alarm when the measure of system performance indicates a presence of system degradation.

3. The method as claimed in claim 1, wherein the industry standard database is the Windows Management Instrumentation database, and the method includes said each distributed processing unit using an operating system to store the performance parameters accumulated by said each of the distributed processing units in the Windows Management Instrumentation database.

4. The method as claimed in claim 1, which includes said each distributed processing unit computing an average of the response time measurements over each of the intervals of time, and storing the average of the response time measurements over each of the intervals of time in the industry standard database in said each distributed processing unit, and which includes retrieving the averages of the response time measurements from the industry standard databases in the distributed processing units, and using the retrieved averages of the response time measurements for determining the measure of system performance.

5. The method as claimed in claim 1, which includes using the measure of system performance for estimating additional processing resources needed to alleviate bottlenecks and optimize system performance.

6. The method as claimed in claim 1, which includes using the performance parameters retrieved from the industry standard databases to compute a measure of metric entropy.

7. The method as claimed in claim 6, wherein the measure of metric entropy ranges from zero to a value greater than one.

8. The method as claimed in claim 6, wherein the measure of metric entropy is computed from the performance parameters retrieved from the industry standard database by computing an average response time over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy from the histogram.

9. The method as claimed in claim 8, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

10. The method as claimed in claim 9, wherein the common duration of time is the duration of the intervals of time across which the response time measurements and workload are accumulated by the distributed processing units.

11. The method as claimed in claim 1, which includes using the performance parameters retrieved from the industry standard databases to determine utilization of the distributed processing units.

12. In a data processing network including distributed processing units, a method of analysis of system performance comprising:

each of the distributed processing units repetitively computing an average response time of said each of the distributed processing units and a number of requests processed by said each of the distributed processing units over respective intervals of time; and

retrieving over the network the average response times and the numbers of requests processed from each of the distributed processing units, and using the retrieved average response times and the numbers of requests processed to determine a measure of system performance and a measure of utilization.

13. The method as claimed in claim 12, which includes triggering an alarm when the measure of system performance indicates a presence of system degradation or when the measure of utilization indicates an overload.

14. The method as claimed in claim 12, which includes using the measure of system performance for estimating additional processing resources needed to alleviate bottlenecks and optimize system performance.

15. The method as claimed in claim 12, wherein the measure of system performance includes a measure of metric entropy computed by computing an average response time over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy from the histogram.

16. The method as claimed in claim 15, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

17. The method as claimed in claim 16, wherein the common duration of time is the duration of the intervals of time over which said each distributed processing unit repetitively computes the average response time of said each distributed processing unit.

18. In a data processing network including distributed processing units, a method of analysis of system performance comprising:

obtaining measurements of response time of the distributed processing units; and

computing a measure of metric entropy of the system performance from the measurements of response time of the distributed processing units by computing an average of the response time measurements over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy of the system performance from the histogram.

19. The method as claimed in claim 18, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

20. The method as claimed in claim 19, which includes each of the distributed processing units repetitively accumulating an average response time of said each of the distributed processing units over respective intervals of time, and wherein the average response time across the distributed processing units is computed by averaging the average response times accumulated by the distributed processing units over the respective intervals of time, and wherein the common duration of time is the duration of the intervals of time over which said each of the distributed processing units repetitively accumulates the average response time of said each of the distributed processing units.

21. The method as claimed in claim 18, wherein the measure of metric entropy ranges from zero to a maximum value greater than 1.

22. In a data processing network including distributed processing units, a method of analysis of system performance comprising:

repetitively computing an average response time of each of the distributed processing units and a number of requests processed by said each of the distributed processing units over respective intervals of time;

computing an aggregate system utilization from the average response times of the distributed processing units and the numbers of requests processed by the distributed processing units over the respective intervals of time; and

preparing a recommendation for additional distributed processing units based on the aggregate system utilization.

23. The method as claimed in claim 22, wherein the additional distributed processing units are recommended to obtain a desired level of aggregate system utilization.

24. In a data processing network including multiple servers performing distributed processing, a method of analysis of system performance comprising:

in each of the servers, repetitively computing an average response time of said each of the servers and a number of requests processed by said each of the servers over respective intervals of time, and repetitively storing the average response time and the number of requests processed in a Windows Management Instrumentation database in said each of the servers; and

accessing over the network the Windows Management Instrumentation database in said each of the servers to retrieve the average response times and the numbers of requests processed, and using the retrieved average response times and numbers of requests processed from the servers to determine a measure of system performance and a measure of utilization, and triggering an alarm when the measure of system performance indicates a presence of system degradation or when the measure of utilization indicates an overload.

25. The method as claimed in claim 24, wherein the measure of system performance includes a measure of metric entropy computed from the retrieved average response times.

26. The method as claimed in claim 25, wherein the computation of the measure of metric entropy of the system from the retrieved average response times includes repetitively computing an average of the retrieved average response times over the servers, computing a histogram of the average of the retrieved average response times over the servers, and computing the metric entropy from the histogram.

27. The method as claimed in claim 26, wherein the histogram is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average of the retrieved average response times over the servers, each pair of values including values of the average of the retrieved average response times over the servers at different times spaced by a common interval of time.

28. The method as claimed in claim 27, which includes using the measure of utilization for recommending additional servers to obtain a desired level of utilization.

29. A data processing network comprising distributed processing units and an analysis engine, each of the distributed processing units having an industry standard database,

wherein each of the distributed processing units is programmed for accumulating performance parameters including response time measurements and workload across intervals of time and storing the performance parameters in the industry standard database in said each of the distributed processing units;

and

wherein the analysis engine is programmed for accessing the industry standard databases over the data processing network to retrieve the performance parameters accumulated by the distributed processing units, and determining a measure of system performance from the retrieved performance parameters.

30. The data processing system as claimed in claim 29, wherein the analysis engine is programmed for triggering an alarm when the measure of system performance indicates a presence of system degradation.

31. The data processing system as claimed in claim 29, wherein the industry standard database is the Windows Management Instrumentation database.

32. The data processing system as claimed in claim 29, wherein each distributed processing unit is programmed for computing an average of the response time measurements over each of the intervals of time, and storing the average of the response time measurements over each of the intervals of time in the industry standard database in said each distributed processing unit, and wherein the analysis engine is programmed for retrieving the averages of the response time measurements from the industry standard databases in the distributed processing units, and using the retrieved averages of the response time measurements for determining the measure of system performance.

33. The data processing system as claimed in claim 29, wherein the analysis engine is programmed for using the measure of system performance for estimating additional processing resources needed to alleviate bottlenecks and optimize system performance.

34. The data processing system as claimed in claim 29, wherein the analysis engine is programmed for computing a measure of metric entropy from the performance parameters retrieved from the industry standard databases.

35. The data processing system as claimed in claim 34, wherein the measure of metric entropy ranges from zero to a value greater than one.

36. The data processing system as claimed in claim 34, wherein the analysis engine is programmed for computing the measure of metric entropy from the performance parameters retrieved from the industry standard database by computing an average response time over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy from the histogram.

37. The data processing system as claimed in claim 36, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

38. The data processing system as claimed in claim 37, wherein the common duration of time is the duration of the intervals of time across which the response time measurements and workload are accumulated by the distributed processing units.

39. The data processing system as claimed in claim 29, wherein the analysis engine is programmed for using the performance parameters retrieved from the industry standard databases to determine utilization of the distributed processing units.

40. A data processing network comprising distributed processing units and an analysis engine;

wherein each of the distributed processing units is programmed for repetitively computing an average response time of said each of the distributed processing units and a number of requests processed by said each of the distributed processing units over respective intervals of time; and

wherein the analysis engine is programmed for retrieving over the network the average response times and the numbers of requests processed from each of the distributed processing units, and using the retrieved average response times and the numbers of requests processed to determine a measure of system performance and a measure of utilization.

41. The data processing system as claimed in claim 40, wherein the analysis engine is programmed for triggering an alarm when the measure of system performance indicates a presence of system degradation or when the measure of utilization indicates an overload.

42. The data processing system as claimed in claim 40, wherein the analysis engine is programmed for using the measure of system performance for estimating additional processing resources needed to alleviate bottlenecks and optimize system performance.

43. The data processing system as claimed in claim 40, wherein the analysis engine is programmed for computing a measure of metric entropy by computing an average response time over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy from the histogram.

44. The data processing system as claimed in claim 43, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

45. The data processing system as claimed in claim 44, wherein the common duration of time is the duration of the intervals of time over which said each distributed processing unit repetitively computes the average response time of said each distributed processing unit.

46. A data processing network comprising distributed processing units, wherein the data processing network is programmed for obtaining measurements of response time of the distributed processing units, and computing a measure of metric entropy of the system performance from the measurements of response time of the distributed processing units by computing an average of the response time measurements over the distributed processing units, computing a histogram of the average response time over the distributed processing units, and computing the measure of metric entropy of the system performance from the histogram.

47. The data processing system as claimed in claim 46, wherein the histogram of the average response time over the distributed processing units is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average response time over the distributed processing units, each pair of values including values of the average response time over the distributed processing units at different times spaced by a common duration of time.

48. The data processing system as claimed in claim 47, wherein each of the distributed processing units is programmed for repetitively accumulating an average response time of said each of the distributed processing units over respective intervals of time, and wherein the data processing network is programmed for computing the average response time across the distributed processing units by averaging the average response times accumulated by the distributed processing units over the respective intervals of time, and wherein the common duration of time is the duration of the intervals of time over which said each of the distributed processing units is programmed to repetitively accumulate the average response time of said each of the distributed processing units.

49. The data processing system as claimed in claim 46, wherein the measure of metric entropy ranges from zero to a maximum value greater than 1.

50. A data processing network comprising distributed processing units, wherein the data processing network is programmed for repetitively computing average response time of each of the distributed processing units and a number of requests processed by said each of the distributed processing units over respective intervals of time, computing an aggregate system utilization from the average response times of the distributed processing units and the numbers of requests processed by the distributed processing units over the respective intervals of time, and preparing a recommendation for additional distributed processing units based on the aggregate system utilization.

51. The data processing system as claimed in claim 50, wherein the data processing network is programmed for recommending the additional distributed processing units to obtain a desired level of aggregate system utilization.

52. A data processing network comprising multiple servers for performing distributed processing, and an analysis engine for analysis of system performance;

wherein each of the servers has a Windows Management Instrumentation database;

wherein each of the servers is programmed for repetitively computing an average response time of said each of the servers and a number of requests processed by said each of the servers over respective intervals of time, and repetitively storing the average response time and the number of requests processed in the Windows Management Instrumentation database in said each of the servers; and

wherein the analysis engine is programmed for accessing over the network the Windows Management Instrumentation database in said each of the servers to retrieve the average response times and the numbers of requests processed, and using the retrieved average response times and numbers of requests processed from the servers to determine a measure of system performance and a measure of utilization, and triggering an alarm when the measure of system performance indicates a presence of system degradation or when the measure of utilization indicates an overload.

53. The data processing system as claimed in claim 52, wherein the measure of system performance includes a measure of metric entropy computed from the retrieved average response times.

54. The data processing system as claimed in claim 53, wherein the analysis engine is programmed to compute the measure of metric entropy of the system from the retrieved average response times by repetitively computing an average of the retrieved average response times over the servers, computing a histogram of the average of the retrieved average response times over the servers, and computing the metric entropy from the histogram.

55. The data processing system as claimed in claim 54, wherein the histogram is an accumulation of occurrences in a two-dimensional phase space of pairs of values of the average of the retrieved average response times over the servers, each pair of values including values of the average of the retrieved average response times over the servers at different times spaced by a common interval of time.

56. The data processing system as claimed in claim 55, wherein the analysis engine is programmed for using the measure of utilization for recommending additional servers to obtain a desired level of utilization.