SASS: Self-Adaptation Using Stochastic Search

Abstract

Future-generation self-adaptive systems will need to be able to optimize for multiple interrelated, difficult-to-measure, and evolving quality properties. To navigate this complex search space, current self-adaptive planning techniques need to be improved. In this position paper, we argue that the research community should more directly pursue the application of stochastic search techniques -- search techniques, such as hill climbing or genetic algorithms, that incorporate an element of randomness -- to self-adaptive systems research. These techniques are well-suited to handling multi-dimensional search spaces and complex problems, situations which arise often for self-adaptive systems. We believe that recent advances in both fields make this a particularly promising research trajectory. We demonstrate one way to apply some of these advances in a search-based planning prototype technique to illustrate both the feasibility and the potential of the proposed research. This strategy informs a number of potentially interesting research directions and problems. In the long term, this general technique could enable sophisticated plan generation techniques that improve domain specific knowledge, decrease human effort, and increase the application of self-adaptive systems.