Improving EECluster to optimize the carbon footprint and operating costs of HPC clusters

Abstract

High Performance Computing Clusters (HPCCs) are essential platforms for solving up-to-date challenges through parallel and distributed applications. Nevertheless, HPCCs have an important economic and environmental impact owing to the large amounts of energy required for their operation. In this work, an improved version of our EECluster software focused on reducing the operating costs and emissions of HPCCs is presented. Multicriteria learning algoritms are leveraged to jointly optimize cluster performance, direct and indirect operating costs both from an economic and an environmental standpoint. EECluster is a mature software that can be integrated with the resource management systems OGE/SGE and PBS/TORQUE.