1 / 7

FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems

Dagstuhl Seminar on Software Engineering for Self-Adaptive Systems. Sam Malek Department of Computer Science George Mason University. FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems. Research Motivation. Plan. Analyze. Problems in practice

donat
Download Presentation

FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Dagstuhl Seminar on Software Engineering for Self-Adaptive Systems Sam Malek Department of Computer Science George Mason University FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems

  2. Research Motivation Plan Analyze Problems in practice • Unwieldy for use, as software engineers are expected to construct complex analytical models • Rigid analytical models cannot handle unanticipated events • Analysis is computationally expensive Traffic Spike Execute Monitor Analytical Models Cyber Attack Architectural Models Internal Failures Quality Objectives

  3. FUSION:Feature-Oriented Self-Adaptation • FUSION addresses these challenges through machine learning • Learning is made possible through feature-oriented adaptation Quality Objectives Feature Model Software Architecture

  4. FUSION Control Flow • Design-time learning • Construct functions that estimate the impact of features on quality goals • Run-time learning • Refine the functions due to (unanticipated) changes in the system Learning also improves the complexity of analysis by removing the irrelevant features Response time = 1.553 F1 − 0.673 F2 + 0.709 F3 + 0.163 F1F3 − 0.843

  5. How does feature-oriented adaptation enable learning? • Configuration space at the feature-level is significantly smaller than the architectural-level • A feature often has the same general (positive/negative) effect on a given quality objective

  6. Ability to Learn Unanticipated Changes • Unanticipated indexing problem in the travel reservation database results in a new response time pattern

  7. Conclusion • Interested in the details: • A. Elkhodary, N. Esfahani, and S. Malek. FUSION: A Framework for Engineering Self-Tuning Self-Adaptive Software Systems. 18th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2010), Santa Fe, NM, Nov. 2010. • Available for download from: http://cs.gmu.edu/~smalek/papers/FSE2010.pdf

More Related