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Proactive Self-Adaptation under Uncertainty: a Probabilistic Model Checking Approach

Gabriel A. Moreno, Javier Cámara, David Garlan and Bradley Schmerl.


In Proceedings of the 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, Bergamo, Italy, 30 August - 4 September 2015.

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Abstract
Self-adaptive systems tend to be reactive and myopic, adapting in response to changes without anticipating what the subsequent adaptation needs will be. Adapting reactively can result in inefficiencies due to the system performing a suboptimal sequence of adaptations. Furthermore, when adaptations have latency, and take some time to produce their e ffect, they have to be started with sufficient lead time so that they complete by the time their eff ect is needed. Proactive latency-aware adaptation addresses these issues by making adaptation decisions with a look-ahead horizon and taking adaptation latency into account. In this paper we present an approach for proactive latency-aware adaptation under uncertainty that uses probabilistic model checking for adaptation decisions. The key idea is to use a formal model of the adaptive system in which the adaptation decision is left underspeci fied through nondeterminism, and have the model checker resolve the nondeterministic choices so that the accumulated utility over the horizon is maximized. The adaptation decision is optimal over the horizon, and takes into account the inherent uncertainty of the environment predictions needed for looking ahead. Our results show that the decision based on a look-ahead horizon, and the factoring of both tactic latency and environment uncertainty, considerably improve the eff ectiveness of adaptation decisions.

Keywords: Latency-aware, Model Checking, Self-adaptation, Self-awareness & Adaptation, Stochastic Games.  
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