An Architectural Framework for the Design and Analysis of Autonomous Adaptive Systems

Autonomous adaptive systems (AAS) have been proposed as a solution to effectively (re)design software so that it can respond to changes in execution environments, without human intervention. In the software engineering community, alternative approaches to the design of AAS have been proposed including solutions based on component technology, design patterns, and resource allocation techniques. A key limitation of the currently available approaches is that they detect constraint violations, but they do not support the prediction of constraint violations. In this work we propose an architectural framework for the design and analysis of autonomous adaptive systems, hereafter referred to as KAROO, which provides a key, new contribution: the capability to predict when a system needs to adapt itself. The results of extensive experimental evaluation of a KAROO-based system are excellent: 100% of the violations are predicted; the system is able to avoid the violations by adapting itself almost 98% of the time. The framework is a novel integration of control-theory-based adaptation, multi-criteria decision making and component-based software engineering techniques.