Performance and energy aware scheduling simulator for HPC: evaluating different resource selection methods

Abstract

SummaryToday, in an energy‐aware society, job scheduling is becoming an important task for computer engineers and system analysts that may lead to a performance per Watt trade‐off of computing infrastructures. Thus, new algorithms, and a simulator of computing environments, may help information and communications technology and data center managers to make decisions with a solid experimental basis. There are several simulators that try to address performance and, somehow, estimate energy consumption, but there are none in which the energy model is based on benchmark data that have been countersigned by independent bodies such as the Standard Performance Evaluation Corporation. This is the reason why we have implemented a performance and energy‐aware scheduling (PEAS) simulator for high‐performance computing. Furthermore, to evaluate the simulator, we propose an implementation of the non‐dominated sorting genetic algorithm‐II (NSGA‐II) algorithm, a fast and elitist multiobjective genetic algorithm, for the resource selection. With the help of the PEAS simulator, we have studied if it is possible to provide an intelligent job allocation policy that may be able to save energy and time without compromising performance. The results of our simulations show a great improvement in response time and power consumption. In most of the cases, NSGA‐II performs better than other ‘intelligent’ algorithms like multiobjective heterogeneous earliest finish time and clearly outperforms the first‐fit algorithm. We demonstrate the usefulness of the simulator for this type of studies and conclude that the superior behavior of multiobjective algorithms makes them recommended for use in modern scheduling systems. Copyright © 2015 John Wiley & Sons, Ltd.