Multi-objective Optimisation in Hybrid Collaborating Adaptive Systems
Conference: ARCS Workshop 2019 - 32nd International Conference on Architecture of Computing Systems
05/20/2019 - 05/21/2019 at Copenhagen, Denmark
Proceedings: ARCS 2019
Pages: 8Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Lesch, Veronika; Krupitzer, Christian (Software Engineering Group, University of Würzburg, Würzburg, Germany)
Tomforde, Sven (Intelligent Systems, University of Passau, Passau, Germany)
Allowing for self-adaptation in technical systems is intended to tackle the ever-increasing complexity resulting from the open, interconnected, and mobile characteristics of information and communication technology. Typically, self-adaptation is established by means of a feedback loop concept, e.g., in terms of the monitor-analyse-plan-execute (-knowledge) loop as known from the Autonomic Computing domain that acts on top of the productive part of a technical system. Two of the major parts of this loop are related to actually steering the behaviour of the productive part: planning what to do and executing this plan. In this paper, we present a novel concept for multi-objective optimisation-based planning of adaptations in autonomous selfadaptive systems. We focus on a subset of self-adaptive systems that deal with resource coordination problems and highlight issues between central planning and decentral execution of plans by autonomous resources. We discuss four application scenarios to illustrate the challenges and the benefits of our concept.