EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this tool

EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this too

Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster

As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters

EECluster: An energy-efficient tool for managing HPC clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this tool

Energy-conscious fuzzy rule-based classifiers for battery operated embedded devices

IEEE International Conference on Fuzzy Systems (2017. Italy

Eco-efficient resource management in HPC clusters through computer intelligence techniques

This research was funded by the Ministry of Economy, Industry and Competitiveness from Spain/FEDER under grants TIN2017-84804-R and RTI2018-098085-B-C44, and by the Regional Ministry of the Principality of Asturias under grant FC-GRUPIN-IDI/2018/000226