An autonomic algorithm for energy efficiency in service centers

This paper addresses the problem of run-time management of a service center energy efficiency by using a context aware self-adapting algorithm. The algorithm adapts the service center energy consumption to the incoming workload by considering service center predefined Green Performance Indicators (GPI) and Key Performance Indicators (KPI). The service center energy performance context is obtained from business applications scheduled to run on service center resources, from service center IT computing resources and from service center facilities. The self-adapting algorithm detects/analyzes service center's current workload and performance changes and decides on adaptation actions for minimizing the energy consumption. A reinforcement learning approach is used to decide the best sequence of actions to be executed to bring the service center resources as close as possible to a GPI and KPI compliant state.