Improving Architecture-Based Self-Adaptation through Resource Prediction

  • Authors:
  • Shang-Wen Cheng;Vahe V. Poladian;David Garlan;Bradley Schmerl

  • Affiliations:
  • School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213;School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213;School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213;School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213

  • Venue:
  • Software Engineering for Self-Adaptive Systems
  • Year:
  • 2009

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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.