Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A reinforcement learning based self-healing algorithm for managing context adaptation
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
An energy aware context model for green IT service centers
ICSOC'10 Proceedings of the 2010 international conference on Service-oriented computing
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The planning process in autonomic software aims at selecting an action from a finite set of alternatives for adaptation. This is an abstruse problem due to the fact that software behavior is usually very complex with numerous number of control variables. This research work focuses on proposing a planning process and specifically an action selection technique based on "Reinforcement Learning" (RL). We argue why, how, and when RL can be beneficial for an autonomic software system. The proposed approach is applied to a simulated model of a news web application. Evaluation results show that this approach can learn to select appropriate actions in a highly dynamic environment. Furthermore, we compare this approach with another technique from the literature, and the results suggest that it can achieve similar performance in spite of no expert involvement.