Particle Swarm Optimization Scheduling for Energy Saving in Cluster Computing Heterogeneous Environments

Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most techniques have been focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Those techniques have received little attention. The large number of computing nodes, heterogeneity and variability of application-tasks are factors that turn the scheduling into an NP-Hard problem. In this paper, we present a novel approach by using a Particle Swarm Optimization (PSO) based heuristic to generate scheduling decisions that minimize the overall energy consumption.