Bottleneck detector application programming interface

• US 9,864,676 B2
• Filed: 08/07/2015
• Issued: 01/09/2018
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Garrett, Charles D., Fraser, Christopher W.
• Primary Examiner(s): Lottich, Joshua P
• Application Number:
  US14/821,352
• Publication Number:
  US 20150347268A1
• Time in Patent Office: 886 Days
• CPC Class Codes: G06F 11/1471 involving logging of persistent data for recovery
• CPC Class Codes: G06F 11/323 Visualisation of programs or trace data
• 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 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 11/3612 by runtime analysis performance monitoring G06F11/3466
• CPC Class Codes: G06F 11/3624 by performing operations on the source code, e.g. via a compiler
• CPC Class Codes: G06F 11/3636 by tracing the execution of the program
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 2201/805 Real-time
• CPC Class Codes: G06F 2201/84 Using snapshots, i.e. a logical point-in-time copy of the data
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 2201/88 Monitoring involving counting
• CPC Class Codes: G06F 8/443 Optimisation
• CPC Class Codes: G06F 8/70 Software maintenance or management
• CPC Class Codes: G06F 9/46 Multiprogramming arrangements
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks

Abstract

An application programming interface may receive workload identifiers and checkpoint identifiers from which bottleneck detection may be performed. Workloads may be tracked through various checkpoints in an application and timestamps collected at each checkpoint. From these data, bottlenecks may be identified in real time or by analyzing the data in a subsequent analysis. The workloads may be processed by multiple devices which may comprise a large application. In some cases, the workloads may be processed by different devices in sequence or in a serial fashion, while in other cases workloads may be processed in parallel by different devices. The application programming interface may be part of a bottleneck detection service which may be sold on a pay-per-use model, a subscription model, or some other payment scheme.

Claims

1. A computing system comprising:

memory containing executable code;

one or more processors, which, when executing the executable code, cause the computing system to be configured with a system architecture for analyzing workloads processed when executing an application in order to identify a bottleneck where time to execute a workload between two checkpoints increases at a greater rate than the workload increases, and wherein the system architecture comprises;

a checkpoint inserter that analyzes the application to identify a plurality of locations for checkpoints, and inserts a checkpoint function call that causes a checkpoint identifier and a timestamp to be captured for each inserted checkpoint;

an execution environment that executes the application in accordance with a workload;

a tracer that responds to the checkpoint function call inserted for each location, and that captures checkpoint identifiers, timestamps, and workload factors for each of said identified checkpoint locations as the application executes;

an analyzer that performs the following computer-implemented method;

identifies a plurality of checkpoints in the application; and

identifies a bottleneck in the executing application by performing a curve fitting analysis for each checkpoint based on one or the other of the workload factor or a time difference based on the timestamp of a current checkpoint as compared to the timestamp of the previous checkpoint, and then analyzes a determined curve by performing one or the other of the following;

(1) analyzing a determined curve for a given checkpoint to determine any anomalies, and if so, identifying the given checkpoint as a bottleneck in the executing application;

or

(2) comparing a determined first curve for a given checkpoint to a second curve for a previous checkpoint to determine any significant differences between the two curves for the current and previous checkpoints, and if so, identifying the given checkpoint as a bottleneck in the executing application.

2. The system of claim 1, wherein said tracer returns a workload identifier in response to each said checkpoint function call.

3. The system of claim 1, wherein said tracer receives a workload identifier with said checkpoint function call.

4. The system of claim 1, wherein said timestamp is transmitted with each said checkpoint function call.

5. The system of claim 1, wherein said timestamp is determined when said checkpoint function call is received.

6. The system of claim 1, wherein said any significant differences between the two curves is based on said first curve having a different slope from said second curve.

7. The system of claim 1, wherein said any significant differences between the two curves is based on said first curve having a different correlation coefficient than said second curve.

8. The system of claim 1, wherein said first curve is a linear curve.

9. The system of claim 1, wherein said first curve is a polynomial curve.

10. The system of claim 1, wherein said timestamp is an incremental time from a beginning time.

11. The system of claim 1, wherein said first curve correlates said timestamp with a load factor.

12. The system of claim 1, wherein said load factor is received as a data stream comprising timestamps.

13. A computer-implemented method for analyzing workloads processed when executing an application in order to identify a bottleneck where time to execute a workload between two checkpoints increases at a greater rate than the workload increases, the computer-implemented method comprising:

analyzing the application to identify a plurality of locations for checkpoints, and inserting a checkpoint function call that causes a checkpoint identifier and a timestamp to be captured for each inserted checkpoint;

executing the application in accordance with a workload;

tracing the application as it executes in accordance with the workload, and as a result of the tracing, capturing tracer data that comprises checkpoint identifiers, timestamps, and workload factors for each of said identified checkpoint locations as the application executes;

based on the tracer data, identifying a plurality of checkpoints in the application; and

identifying a bottleneck in the executing application by performing a curve fitting analysis for each checkpoint based on one or the other of the workload factor or a time difference based on the timestamp of a current checkpoint as compared to the timestamp of the previous checkpoint, and then analyzing a determined curve by performing one or the other of the following;

(1) analyzing a determined curve for a given checkpoint to determine any anomalies, and if so, identifying the given checkpoint as a bottleneck in the executing application;

or

(2) comparing a determined first curve for a given checkpoint to a second curve for a previous checkpoint to determine any significant differences between the curves for the current and previous checkpoints, and if so, identifying the given checkpoint as a bottleneck in the executing application.

14. The computer-implemented method of claim 13, wherein a workload identifier is returned in response to each said checkpoint function call.

15. The computer-implemented method of claim 13, wherein a workload identifier is received with each said checkpoint function call.

16. The computer-implemented method of claim 13, wherein said timestamp is transmitted with each said checkpoint function call.

17. The computer-implemented method of claim 13, wherein said timestamp is determined when said checkpoint function call is received.

18. The computer-implemented method of claim 13, wherein said any significant differences between the two curves is based on said first curve having a different slope from said second curve.

19. The computer-implemented method of claim 13, wherein said any significant differences between the two curves is based on said first curve having a different correlation coefficient than said second curve.

20. The computer-implemented method of claim 13, wherein said first curve is a linear curve.

21. The computer-implemented method of claim 13, wherein said first curve is a polynomial curve.

22. The computer-implemented method of claim 13, wherein said timestamp is an incremental time from a beginning time.

23. The computer-implemented method of claim 13, wherein said first curve correlates said timestamp with a load factor.

24. The computer-implemented method of claim 13, wherein said load factor is received as a data stream comprising timestamps.