research-article

FUSION: a framework for engineering self-tuning self-adaptive software systems

Authors:

Ahmed Elkhodary,

Naeem Esfahani,

Sam MalekAuthors Info & Claims

FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering

Pages 7 - 16

https://doi.org/10.1145/1882291.1882296

Published: 07 November 2010 Publication History

Abstract

Self-adaptive software systems are capable of adjusting their behavior at run-time to achieve certain objectives. Such systems typically employ analytical models specified at design-time to assess their characteristics at run-time and make the appropriate adaptation decisions. However, prior to system's deployment, engineers often cannot foresee the changes in the environment, requirements, and system's operational profile. Therefore, any analytical model used in this setting relies on underlying assumptions that if not held at run-time make the analysis and hence the adaptation decisions inaccurate. We present and evaluate FeatUre-oriented Self-adaptatION (FUSION) framework, which aims to solve this problem by learning the impact of adaptation decisions on the system's goals. The framework (1) allows for automatic online fine-tuning of the adaptation logic to unanticipated conditions, (2) reduces the upfront effort required for building such systems, and (3) makes the run-time analysis of such systems very efficient.

References

[1]

Carroll, D.J. 2002. Statistics Made Simple for School Leaders: Data-Driven Decision Making. ScarecrowEducation.

[2]

Cheng, B., et al. 2009. Software Engineering for Self-Adaptive Systems: A Research Roadmap. Software Engineering for Self-Adaptive Systems, LNCS. 1--26.

Digital Library

[3]

Edwards, G., Malek, S., Medvidovic, N. 2007. Scenario-Driven Dynamic Analysis of Distributed Architectures. Int'l Conf. on Fundamental Approaches to Software Engineering (Braga, Portugal, March 2007), 125.

Digital Library

[4]

Friedman, J.H. and Roosen, C.B. 1995. An introduction to multivariate adaptive regression splines. Statistical Methods in Medical Research. 4, 3 (Sep. 1995), 197--217.

[5]

Garlan, D., Cheng, S.W. et al. 2004. Rainbow: Architecture-Based Self--Adaptation with Reusable Infrastructure. IEEE Computer. 37, 10 (Oct. 2004), 46--54.

Digital Library

[6]

Georgas, J.C. and Taylor, R.N. 2004. Towards a knowledge-based approach to architectural adaptation management. Workshop on Self-healing Systems (Newport Beach, California, October 2004), 59--63.

Digital Library

[7]

Gomaa, H. 2004. Designing Software Product Lines with UML: From Use Cases to Pattern-Based Software Architectures. Addison-Wesley Professional.

Digital Library

[8]

Gross, D. and Harris, C.M. 1985. Fundamentals of queueing theory (2nd ed.). John Wiley & Sons, Inc.

Digital Library

[9]

Jordan, M.I. and Jacobs, R.A. 1994. Hierarchical mixtures of experts and the EM algorithm. Neural Comput. 6, 2 (1994), 181--214.

Digital Library

[10]

Kephart, J.O. and Chess, D.M. 2003. The Vision of Autonomic Computing. IEEE Computer. 36(1), 41--50.

Digital Library

[11]

Kim, D. and Park, S. 2009. Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software. Workshop on Softw. Eng. For Adaptive and Self-Managing Systems (Vancouver, Canada, May 2009), 76--85.

Digital Library

[12]

Kleppe, A., Warmer, J. et al. 2003. MDA Explained: The Model Driven Architecture: Practice and Promise. Addison-Wesley Professional.

Digital Library

[13]

Kramer, J. and Magee, J. 2007. Self-Managed Systems: an Architectural Challenge. Int'l Conf. on Software Engineering (Minneapolis, MN, May 2007), 259--268.

Digital Library

[14]

Malek, S., et al. 2005. A Style-Aware Architectural Middleware for Resource--Constrained, Distributed Systems. IEEE Trans. Softw. Eng. 31, 3 (2005), 256--272.

Digital Library

[15]

Menascé, D.A., Ewing, J.M. et al. 2010. A framework for utility-based service oriented design in SASSY. Joint WOSP/SIPEW Int'l Conf. on Performance engineering (San Jose, CA, January 2010), 27--36.

Digital Library

[16]

Oreizy, P., Medvidovic, N., Taylor, R. 1998. Architecture-based runtime software evolution. Int'l Conf. on Software Engineering (Kyoto, Japan, April 1998), 177--186.

Digital Library

[17]

Poladian, V., et al. 2004. Dynamic Configuration of Resource-Aware Services. Int'l Conf. on Software Engineering (Scotland, UK, May 2004), 604--613.

Digital Library

[18]

Sykes, D., et al. 2008. From goals to components: a combined approach to self-management. Int'l Workshop on Software Engineering for Adaptive and Self-Managing Systems (Leipzig, Germany, May 2008), 1--8.

Digital Library

[19]

Tesauro, G., et al. 2006. A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. Int'l Conf. on Autonomic Computing (Dublin, Ireland, June 2006), 65--73.

Digital Library

[20]

WEKA. http://www.cs.waikato.ac.nz/ml/weka/.

Cited By

Banerjee AChoppella V(2025)Control Software Engineering Approaches for Cyber-Physical Systems: A Systematic Mapping StudyACM Transactions on Cyber-Physical Systems10.1145/37047379:1(1-33)Online publication date: 12-Jan-2025
https://dl.acm.org/doi/10.1145/3704737
Hashmi SDam HBaruwal Chhetri MUzunov AColman AVo Q(2025)Proactive self-exploration: Leveraging information sharing and predictive modelling for anticipating and countering adversariesExpert Systems with Applications10.1016/j.eswa.2024.126118267(126118)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126118
Li NZhang MLi JAdepu SKang EJin Z(2024)A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/365294919:2(1-49)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3652949
Show More Cited By

Recommendations

Self-adaptive software: Landscape and research challenges

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-...
Extending MAPE-K to support human-machine teaming
SEAMS '22: Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems

The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm ...
Beyond the rainbow: self-adaptive failure avoidance in configurable systems
FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering

November 2010

302 pages

ISBN:9781605587912

DOI:10.1145/1882291

General Chair:
Gruia-Catalin Roman
Washington University in St. Louis, USA
,
Program Chair:
André van der Hoek
University of California, Irvine, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGSOFT/FSE'10

Sponsor:

SIGSOFT

SIGSOFT/FSE'10: 18th ACM SIGSOFT Symposium on the Foundations of Software Engineering

November 7 - 11, 2010

New Mexico, Santa Fe, USA

Acceptance Rates

Overall Acceptance Rate 17 of 128 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

144
Total Citations
View Citations
1,159
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Banerjee AChoppella V(2025)Control Software Engineering Approaches for Cyber-Physical Systems: A Systematic Mapping StudyACM Transactions on Cyber-Physical Systems10.1145/37047379:1(1-33)Online publication date: 12-Jan-2025
https://dl.acm.org/doi/10.1145/3704737
Hashmi SDam HBaruwal Chhetri MUzunov AColman AVo Q(2025)Proactive self-exploration: Leveraging information sharing and predictive modelling for anticipating and countering adversariesExpert Systems with Applications10.1016/j.eswa.2024.126118267(126118)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126118
Li NZhang MLi JAdepu SKang EJin Z(2024)A Game-Theoretical Self-Adaptation Framework for Securing Software-Intensive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/365294919:2(1-49)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3652949
S AJoon RDevi SKhandan KHoward EM R(2024)Self-Adaptive Electronics using Artificial Neural Networks2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)10.1109/ICONSTEM60960.2024.10568609(1-6)Online publication date: 4-Apr-2024
https://doi.org/10.1109/ICONSTEM60960.2024.10568609
Groner RCaldas RWohlrab R(2024)Meta-Adaptation Goals: Leveraging Feedback Loop Requirements for Effective Self-Adaptation2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C63493.2024.00037(91-96)Online publication date: 16-Sep-2024
https://doi.org/10.1109/ACSOS-C63493.2024.00037
Morisaki SWakimoto MKasai N(2024)Assessing Critical Adaptations in Automated Adaptive Software Systems by Stage DecompositionIEEE Access10.1109/ACCESS.2024.336027512(17859-17875)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3360275
Xu MLiu RTahvildari LStoodley MOnut IShirani POnut IOnut IBranco P(2023)An Educational Course on Self-Adaptive Systems using IBM TechnologiesProceedings of the 33rd Annual International Conference on Computer Science and Software Engineering10.5555/3615924.3615939(143-148)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.5555/3615924.3615939
Li JNejati SSabetzadeh M(2023)Using Genetic Programming to Build Self-Adaptivity into Software-Defined NetworksACM Transactions on Autonomous and Adaptive Systems10.1145/3616496Online publication date: 17-Aug-2023
https://dl.acm.org/doi/10.1145/3616496
Weyns DGerostathopoulos IAbbas NAndersson JBiffl SBrada PBures TDi Salle AGalster MLago PLewis GLitoiu MMusil AMusil JPatros PPelliccione P(2023)Self-Adaptation in Industry: A SurveyACM Transactions on Autonomous and Adaptive Systems10.1145/358922718:2(1-44)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1145/3589227
Petrovska AHutzelmann TKugele SHong JLanperne MPark JCerny TShahriar H(2023)A Theoretical Framework for Self-Adaptive Systems: Specifications, Formalisation, and Architectural ImplicationsProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577665(1440-1449)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577665
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten