Decision Making in Autonomic Computing Systems: Comparison of Approaches and Techniques

Autonomic computing systems adapt themselves thousands of times a second, to accomplish their goal despite changing environmental conditions and demands. The literature reports many decision mechanisms, but in most realizations a single one is applied. This paper compares of some state-of-the-art decision making approaches, applied to a self-optimizing autonomic system that allocates resources to a software application providing performance feedback at runtime, via the Application Heartbeat framework. The investigated decision mechanisms range from heuristics to control theory and machine learning: results are compared by means of case studies using standard benchmarks.