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Stochastic Game Analysis and Latency Awareness for Proactive Self-Adaptation

Javier Cámara, Gabriel A. Moreno and David Garlan.

In 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Hyderabad, India, 2-3 June 2014.

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Although diff erent approaches to decision-making in self- adaptive systems have shown their e ffectiveness in the past by factoring in predictions about the system and its environment (e.g., resource availability), no proposal considers the latency associated with the execution of tactics upon the target system. However, di fferent adaptation tactics can take diff erent amounts of time until their eff ects can be observed. In reactive adaptation, ignoring adaptation tactic latency can lead to suboptimal adaptation decisions (e.g., activating a server that takes more time to boot than the transient spike in traffic that triggered its activation). In proactive adaptation, taking adaptation latency into account is necessary to get the system into the desired state to deal with an upcoming situation. In this paper, we introduce a formal analysis technique based on model checking of stochastic multiplayer games (SMGs) that enables us to quantify the potential benefi ts of employing di fferent types of algorithms for self-adaptation. In particular, we apply this technique to show the potential benefi t of considering adaptation tactic latency in proactive adaptation algorithms. Our results show that factoring in tactic latency in decision making improves the outcome of adaptation. We also present an algorithm to do proactive adaptation that considers tactic latency, and show that it achieves higher utility than an algorithm that under the assumption of no latency is optimal.

Keywords: Assurance, Landmark, Latency-aware, Model Checking, Self-adaptation.  
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