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Improving Architecture-Based Self-Adaption Through Resource Prediction

Vahe Poladian, Shang-Wen Cheng, David Garlan and Bradley Schmerl.


In Betty H.C. Cheng, Rogério de Lemos, Holger Giese, Paola Inverardi and Jeff Magee editors, Software Engineering for Self-Adaptive Systems, Vol. 5525 of Lecture Notes in Computer Science, Chapter 15, LNCS, 2008.

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Abstract
An increasingly important concern for modern systems design is how best to incorporate self-adaptation into systems so as to improve their ability to dynamically respond to faults, resource variation, and changing user needs. One promising approach is to use architectural models as a basis for monitoring, problem detection, and repair selection. While this approach has been shown to yield positive results, current systems use a reactive approach: they respond to problems only when they occur. In this paper we argue that self-adaptation can be improved by adopting an anticipatory approach in which predictions are used to inform adaptation strategies. We show how such an approach can be incorporated into an architecture-based adaptation framework and demonstrate the benefits of the approach.

Keywords: Rainbow, Resource prediction, Self-adaptation.  
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