Article

Designing search based adaptive systems: a quantitative approach

Authors:

Marin LitoiuAuthors Info & Claims

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

Pages 7 - 16

https://doi.org/10.1145/2593929.2593935

Published: 02 June 2014 Publication History

Abstract

Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In cloud environment, a designer has to also consider and evaluate different control points, i.e., those variables that affect the quality of the software system. This paper presents a method for eliciting, evaluating and ranking control points for web applications deployed in cloud environments. The proposed method consists of several phases that take high-level stakeholders' adaptation goals and transform them into lower level MAPE-K loop control points. The MAPE-K loops are then activated at runtime using search-based algorithms. We conducted several experiments to evaluate the different phases of our methodology.

References

[1]

Jesper Andersson, Rogerio De Lemos, Sam Malek, and Danny Weyns. Modeling dimensions of self-adaptive software systems. In Software engineering for self-adaptive systems, pages 27–47. Springer, 2009.

Digital Library

[2]

Luciano Baresi and Liliana Pasquale. Live goals for adaptive service compositions. In Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’10, pages 114–123. ACM, 2010.

Digital Library

[3]

Yuriy Brun, Ron Desmarais, Kurt Geihs, Marin Litoiu, Antonia Lopes, Mary Shaw, and Michael Smit. A design space for self-adaptive systems. In Software Engineering for Self-Adaptive Systems II, volume 7475 of Lecture Notes in Computer Science, pages 33–50. Springer Berlin Heidelberg, 2013.

[4]

Brun, Yuriy et al. In Software Engineering for Self-Adaptive Systems, chapter Engineering Self-Adaptive Systems Through Feedback Loops, pages 48–70. Springer-Verlag, 2009.

Digital Library

[5]

Cheng, Betty H. et al. Software engineering for self-adaptive systems. chapter Software Engineering for Self-Adaptive Systems: A Research Roadmap, pages 1–26. Springer-Verlag, 2009.

Digital Library

[6]

Lawrence Chung, B Nixon, E Yu, and J Mylopoulos. Non-functional requirements. Software Engineering, 2000.

[7]

Luiz Marcio Cysneiros and Eric Yu. Non-functional requirements elicitation. In Perspectives on software requirements, pages 115–138. Springer, 2004.

[8]

A.G. Ganek and T. A. Corbi. The dawning of the autonomic computing era. IBM Systems Journal, 42(1):5–18, 2003.

Digital Library

[9]

D. Garlan, Shang-Wen Cheng, An-Cheng Huang, B. Schmerl, and P. Steenkiste. Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer, 37(10):46–54, 2004.

Digital Library

[10]

Markus C Huebscher and Julie A McCann. A survey of autonomic computing-degrees, models, and applications. ACM Computing Surveys (CSUR), 40(3):7, 2008.

Digital Library

[11]

J.O. Kephart and D.M. Chess. The vision of autonomic computing. Computer, 36(1):41–50, 2003.

Digital Library

[12]

J. Kramer and J. Magee. Self-managed systems: an architectural challenge. In Future of Software Engineering, 2007. FOSE ’07, pages 259–268, 2007.

Digital Library

[13]

Jim Li, John Chinneck, Murray Woodside, Marin Litoiu, and Gabriel Iszlai. Performance model driven qos guarantees and optimization in clouds. In Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, pages 15–22. IEEE Computer Society, 2009.

Digital Library

[14]

Jim Zw Li, Murray Woodside, John Chinneck, and Marin Litoiu. Cloudopt: Multi-goal optimization of application deployments across a cloud. In Proceedings of the 7th International Conference on Network and Services Management, CNSM ’11, pages 162–170. International Federation for Information Processing, 2011.

Digital Library

[15]

Marin Litoiu and Cornel Barna. A performance evaluation framework for web applications. Journal of Software: Evolution and Process, 25(8):871–890, 2013.

[16]

Marin Litoiu, Murray Woodside, and Tao Zheng. Hierarchical model-based autonomic control of software systems. In ACM SIGSOFT Software Engineering Notes, volume 30, pages 1–7. ACM, 2005.

Digital Library

[17]

Donella Meadows. Places to intervene in a system. Whole Earth, 91:78–84, 1997.

[18]

Zbigniew Michalewicz, Dipankar Dasgupta, Rodolphe G Le Riche, and Marc Schoenauer. Evolutionary algorithms for constrained engineering problems. Computers & Industrial Engineering, 30(4):851––870, 1996.

Digital Library

[19]

Oreizy, Peyman et al. An architecture-based approach to self-adaptive software. Intelligent Systems and Their Applications, IEEE, 14(3):54–62, 1999.

Digital Library

[20]

Nauman A Qureshi, Anna Perini, Neil A Ernst, and John Mylopoulos. Towards a continuous requirements engineering framework for self-adaptive systems. In Requirements@ Run. Time (RE@ RunTime), 2010 First International Workshop on, pages 9–16. IEEE, 2010.

[21]

S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2009.

Digital Library

[22]

T. L. Saaty. The Analytic Hierarchy Process. Mcgraw-Hill International, 1980.

[23]

Mazeiar Salehie and Ladan Tahvildari. Self-adaptive software: Landscape and research challenges. ACM Trans. Auton. Adapt. Syst., 4(2):14:1–14:42, May 2009.

Digital Library

[24]

Patrizia Scandurra, Claudia Raibulet, Pasqualina Potena, Raffaela Mirandola, and Rafael Capilla. A layered coordination framework for optimizing resource allocation in adapting cloud-based applications. In Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC ’12, pages 471–472. ACM, 2012.

Digital Library

[25]

V´ıtor E Silva Souza, Alexei Lapouchnian, William N Robinson, and John Mylopoulos. Awareness requirements for adaptive systems. In Proceedings of the 6th international symposium on Software engineering for adaptive and self-managing systems, pages 60–69. ACM, 2011.

Digital Library

[26]

Michael Smit, Mark Shtern, Bradley Simmons, and Marin Litoiu. Partitioning applications for hybrid and federated clouds. In Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research, pages 27–41. IBM Corp., 2012.

Digital Library

[27]

Alice E. Smith and David W. Coit. Constraint handling techniques-penalty functions. In Handbook of Evolutionary Computation. Oxford University Press and Institute of Physics Publishing, 1997.

[28]

Jeffrey S Vetter and Daniel A Reed. Real-time performance monitoring, adaptive control, and interactive steering of computational grids. International Journal of High Performance Computing Applications, 14(4):357–366, 2000.

Digital Library

Cited By

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
Gerostathopoulos IPlasil FPrehofer CThomas JBischl B(2021)Automated Online Experiment-Driven Adaptation–Mechanics and Cost AspectsIEEE Access10.1109/ACCESS.2021.30718099(58079-58087)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3071809
Hidaka SHu ZLitoiu MLiu LMartin PPeng XWang GYu Y(2019)Design and Engineering of Adaptive Software SystemsEngineering Adaptive Software Systems10.1007/978-981-13-2185-6_1(1-33)Online publication date: 15-Jan-2019
https://doi.org/10.1007/978-981-13-2185-6_1
Show More Cited By

Index Terms

Designing search based adaptive systems: a quantitative approach
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

Recommendations

Designing Adaptive Applications Deployed on Cloud Environments
Special Section on Best Papers from SEAMS 2014 and Regular Articles

Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In a cloud environment, a designer has to consider and evaluate different control points, that is, those variables that ...
CloudCmp: comparing public cloud providers
IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement

While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a ...
Runtime Performance Management for Cloud Applications with Adaptive Controllers
ICPE '18: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering

Adaptability is an expected property of modern software systems in order to cope with changes in the environment by self-adjusting their structure and behaviour. Robustness is a crucial component of adaptability and it refers to the ability of the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SEAMS 2014: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

June 2014

174 pages

ISBN:9781450328647

DOI:10.1145/2593929

General Chair:
Gregor Engels
University of Paderborn, Germany
,
Program Chair:
Nelly Bencomo
Aston University, UK

Copyright © 2014 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

In-Cooperation

TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

ICSE '14

Sponsor:

SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

June 2 - 3, 2014

Hyderabad, India

Acceptance Rates

Overall Acceptance Rate 17 of 31 submissions, 55%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
350
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
Gerostathopoulos IPlasil FPrehofer CThomas JBischl B(2021)Automated Online Experiment-Driven Adaptation–Mechanics and Cost AspectsIEEE Access10.1109/ACCESS.2021.30718099(58079-58087)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3071809
Hidaka SHu ZLitoiu MLiu LMartin PPeng XWang GYu Y(2019)Design and Engineering of Adaptive Software SystemsEngineering Adaptive Software Systems10.1007/978-981-13-2185-6_1(1-33)Online publication date: 15-Jan-2019
https://doi.org/10.1007/978-981-13-2185-6_1
Angelopoulos KPapadopoulos ASouza VMylopoulos J(2018)Engineering Self-Adaptive Software SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/310574813:1(1-27)Online publication date: 16-Apr-2018
https://dl.acm.org/doi/10.1145/3105748
Angelopoulos KPapadopoulos ASilva Souza VMylopoulos J(2016)Model predictive control for software systems with CobRAProceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1145/2897053.2897054(35-46)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2897053.2897054
Zoghi PShtern MLitoiu MGhanbari H(2016)Designing Adaptive Applications Deployed on Cloud EnvironmentsACM Transactions on Autonomous and Adaptive Systems10.1145/282289610:4(1-26)Online publication date: 13-Jan-2016
https://dl.acm.org/doi/10.1145/2822896
Angelopoulos KBasak Aydemir FGiorgini PMylopoulos J(2016)Solving the next adaptation problem with prometheus2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2016.7549328(1-10)Online publication date: Jun-2016
https://doi.org/10.1109/RCIS.2016.7549328
Beigi-Mohammadi NKhazaei HShtern MBarna CLitoiu M(2016)On Efficiency and Scalability of Software-Defined Infrastructure for Adaptive Applications2016 IEEE International Conference on Autonomic Computing (ICAC)10.1109/ICAC.2016.39(25-34)Online publication date: Jul-2016
https://doi.org/10.1109/ICAC.2016.39
Coker ZGarlan DLe Goues CInverardi PSchmerl B(2015)SASSProceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.5555/2821357.2821386(168-174)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2821357.2821386
Coker ZGarlan DGoues C(2015)SASSProceedings of the 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2015.16(168-174)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1109/SEAMS.2015.16

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.

Media

Figures

Other

Tables

View Table of Contents