A Multi-objective Proposal Based on Firefly Behaviour for Green Scheduling in Grid Systems

Global warming and climate change are threats that the planet is facing nowadays. Green computing has emerged as a challenge to reduce the energy consumption and pollution footprints of computers. Grid Computing could match the principles of Green Computing as it could exploit and efficiently use processors’ computing power. This paper presents a swarm multi-objective optimization algorithm for scheduling experiments (the job execution) on the Grid. Multi-Objective Firefly Algorithm (MO-FA) is inspired by the brightness attraction among fireflies. One of the main contributions of this work is that the increasing firefly brightness is interpreted as an improvement in response time and energy savings. This would fulfill both conflicting objectives of Grid users: execution time and energy consumption. Results show that MO-FA is a reliable method according to its interquartile range and its comparison with the standard and well-known multi-objective algorithm NSGA-II. Moreover, it performs better than actual grid schedulers as the Workload Management System (WMS) and the Deadline Budget Constraint (DBC).