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Model Predictive Control for Software Systems with CobRA
University of Brighton.
Lund University.ORCID-id: 0000-0002-1364-8127
University of Toronto.
2016 (engelsk)Inngår i: SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Austin, Texas, United States: ACM , 2016, s. 35-46Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This paper examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed theory and myriads of successful applications. The paper focuses on modelling the dynamic relationship between requirements and possible adaptations. It then proposes a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost-function. This is accomplished through a model-based framework for designing self-adaptive software systems that can guarantee a certain level of requirements satisfaction over time, by dynamically composing adaptation strategies when necessary. The proposed framework is illustrated and evaluated through a simulation of the Meeting-Scheduling System exemplar.

sted, utgiver, år, opplag, sider
Austin, Texas, United States: ACM , 2016. s. 35-46
Emneord [en]
awareness requirements, model predictive control, self-adaptive systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-33791DOI: 10.1145/2897053.2897054ISBN: 978-1-4503-4187-5 (tryckt)OAI: oai:DiVA.org:mdh-33791DiVA, id: diva2:1048552
Konferanse
Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 16, 16 May 2016, Austin, Texas, United States
Tilgjengelig fra: 2016-11-21 Laget: 2016-11-21 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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