Abstract
Execution time optimization is one of the most important objectives to accomplish for experiments launched on grid environments. However, the computing community is becoming more conscious about energy savings, seeking their optimization. In this work, both execution time and energy consumption are optimized through two swarm and multi-objective algorithms based on both physics and biology fields. On the one hand, multi-objective gravitational search algorithm (MOGSA) is inspired by the gravity forces between the planet masses. On the other hand, Multi-Objective Firefly Algorithm is based on the light attraction between the fireflies. These swarm algorithms are compared with the standard multi-objective algorithm non-dominated sorting genetic algorithm II to study their efficiency as multi-objective algorithms. Moreover, the best algorithm proposed, MOGSA, is compared with MOHEFT (a multi-objective version of one of the most-used algorithms in workflow scheduling, HEFT), and also with two real grid schedulers: Workload Management System and deadline budget constraint. Results show the superior performance of MOGSA regarding the others.
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