Proactive Autonomic Cloud Application Management

Applications running in the Cloud can adapt to the varying demands by autonomic management of their resource configurations. The reconfiguration can be done as a reaction to the changed situation, or proactively to ensure the good performance of the application at some future point. However, it is difficult to predict the future behaviour of the application as it depends both on the changing contexts and the reconfiguration actions. This paper describes the approach for proactive autonomic Cloud application management and introduces a distinction between ‘independent metrics’,’performance metrics’ influenced by the reconfigurations, and ‘performance indicators’ related to the application’s utility and reconfigurations. It is shown how performance metrics and performance indicators can be learned as regression functions, and used in the proactive autonomic Cloud application optimization. Finally, the results of an evaluation by simulation show that the proposed approach is more accurate than reactive application control, and it gives better results in terms of the application’s utility.