Performance engineering and the application life cycle

• US 7,890,315 B2
• Filed: 05/11/2006
• Issued: 02/15/2011
• Est. Priority Date: 12/29/2005
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Meier, John D., Vasireddy, Srinath
• Primary Examiner(s): Shah; Kamini S
• Assistant Examiner(s): Alhija; Saif A
• Application Number:
  US11/382,861
• Publication Number:
  US 20070156375A1
• Time in Patent Office: 1,741 Days
• Field of Search: 703/6, 703/1, 703/22
• US Class Current: 703/22
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations

Abstract

A novel approach to performance engineering that leverages expertise to enable a user to design, build and deploy reliable and efficient applications is disclosed. In doing so, the innovation discloses novel techniques and mechanisms that integrate performance into the application development lifecycle and to adapt current software engineering practices and methodologies to include specific performance related activities. These activities include identifying performance objectives, creating performance models, applying performance-related design guidelines, conducting performance-related design inspections, performing regular code inspections, testing for performance, and conducting performance-related deployment inspections.

Claims

1. A system that facilitates performance engineering of an application, comprising:

one or more computer processors;

one or more computer storage media having stored thereon computer-executable instructions in the form of;

a performance engineering component, executable by the one or more computer processors, that includes a plurality of performance engineering activities, wherein each of the performance engineering activities are available throughout an entire life cycle of development of an application, wherein the performance engineering component produces a metric, wherein the performance engineering activities are defined by a performance frame specifically for the application, and wherein the performance frame includes a pattern-based information model for the application defining specific performance objectives;

a performance integration component, executable by the one or more computer processors, that integrates a subset of the plurality of performance engineering activities into development of the application, such that all of the plurality of performance engineering activities are available throughout the entire life cycle, while some, but not all, of the plurality of performance engineering activities are implementable at one or more stages of the life cycle of development of the application;

and a machine leaning and reasoning (MLR) component, executable by the one or more computer processors, providing artificial intelligence, wherein the MLR component is communicatively coupled to the performance engineering component and the performance integration component, and wherein the MLR component maps an input attribute vector to a confidence in automatically establishing a baseline and at least some performance objectives for the application.

2. The system of claim 1, the plurality of performance engineering activities includes at least one of identifying performance objectives, identifying performance models, applying performance-related design guidelines, conducting performance-related design inspections, performing regular performance-related code inspections, implementing performance testing, and conducting performance-related deployment inspections.

3. The system of claim 1, the performance integration component integrates the subset of the plurality of performance engineering activities based upon a phase of the development of the application.

4. The system of claim 3, the subset of the plurality of performance engineering activities includes a performance objectives identification activity and the phase is a requirements and analysis phase.

5. The system of claim 3, the subset of the plurality of performance engineering activities includes at least one of a performance design guidelines activity, a performance modeling activity and a performance architecture and design inspection activity and the phase is an architecture and design phase.

6. The system of claim 3, the subset of the plurality of performance engineering activities includes a performance code inspection activity and the phase is an implementation phase.

7. The system of claim 3, the subset of the plurality of performance engineering activities includes a security testing activity and the phase is a testing phase.

8. The system of claim 3, the subset of the plurality of performance engineering activities includes at least one of a performance testing and a performance deployment inspection and the phase is at least one of a verification and a release phase.

9. The system of claim 3, the subset of the plurality of performance engineering activities includes a capacity planning activity and the phase is a maintenance phase.

10. The system of claim 1, the performance integration component comprises a performance objectives identification component that interfaces with a user to identify a plurality of performance objectives.

11. The system of claim 10, the performance objectives identification component comprises a performance frame component that defines a set of performance-related categories based upon a type of the application.

12. The system of claim 11, the set of performance-related categories comprises at least one of coupling and cohesion, resource management, communication, caching, state management, concurrency and data structures/algorithms.

13. A computer-implemented method of engineering an application, comprising:

using one or more processors of a computing system, executing computer executable instructions stored on one or more computer storage media, wherein execution of the computer executable instructions by the one or more processors causes the computing system to;

identify a performance category;

identify a performance objective based at least in part upon the performance category, wherein the performance category is defined by a predominance frame specifically for the application, and wherein the performance frame includes a pattern-based information model for the application defining specific performance objectives, wherein identifying the performance objective includes using a machine learning and reasoning (MLR) component providing artificial intelligence, wherein the MLR component maps an input attribute vector to a confidence in automatically establishing the performance objective based at least in part upon the performance category;

identify a budget based at least in part upon the performance objective;

after identifying the budget;

allocate the identified budget to a plurality of processing steps; and

integrate the identified budget into development of the application throughout various stages of a life cycle of the development of the application;

produce a metric for performance engineering; and

integrate a performance engineering activity based at least in part upon the performance objective.

14. The computer-implemented method of claim 13, further comprising establishing performance design guidelines based at least in part upon the performance objective.

15. The computer-implemented method of claim 13 further comprising reviewing the application from an architectural and design performance perspective.

16. The computer-implemented method of claim 13, further comprising identifying a performance model based at least in part upon the objective.

17. The computer-implemented method of claim 16, the act of identifying the performance model comprises:

identifying a usage scenario;

identifying a workload based at least in part upon the usage scenario;

identifying a performance objective based at least in part upon the workload; and

identifying a plurality of processing steps based upon the performance objective.

18. The method of claim 17, further comprising:

evaluating the application based upon the performance model; and

validating the performance objective based upon the performance model.

19. A system embodied that facilitates performance engineering of an application, comprising:

one or more computer processors;

one or more computer storage media having stored thereon computer-executable instructions in the form of;

a performance engineering component, executable by the one or more computer processors, that includes a plurality of performance engineering activities available throughout an entirety of a life cycle of development of an application, the performance engineering component for producing a metric, wherein the performance engineering activities are defined by a performance frame specifically for the application, the performance frame including a pattern-based information model for the application defining specific performance objectives;

a performance integration component, executable by the one or more computer processors, that integrates a subset of the plurality of performance engineering activities into development of the application, such that all of the plurality of performance engineering activities are available and modifiable throughout the entire life cycle, while some, but not all, of the plurality of performance engineering activities are implementable at one or more stages of the life cycle of development of the application, wherein the performance integration component selects the subset based on a stage of development; and

a machine learning, and reasoning (MLR) component, executable by the one or more computer processors providing artificial intelligence, wherein the MLR component is communicatively coupled to the performance engineering component and the performance integration component, and wherein the MLR component maps an input attribute vector to a confidence in automatically establishing a baseline and at least some performance objectives for the application,

wherein the performance engineering activities are selected from the group consisting of identifying performance objectives, identifying performance models, applying performance-related design guidelines, conducting performance-related design inspections, performing regular performance-related code inspections, implementing performance testing, and conducting performance-related deployment inspections.