Taming uncertainty in self-adaptive software

Abstract

Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation decisions. This has been one of the key obstacles to wide-spread adoption of self-adaption techniques in risk-averse real-world settings. In this paper, we describe an approach, called POssIbilistic SElf-aDaptation (POISED), for tackling the challenge posed by uncertainty in making adaptation decisions. POISED builds on possibility theory to assess both the positive and negative consequences of uncertainty. It makes adaptation decisions that result in the best range of potential behavior. We demonstrate POISED's application to the problem of improving a software system's quality of service via runtime reconfiguration of its customizable software components. We have extensively evaluated POISED using a prototype of a robotic software system.