Energy optimization for task scheduling in distributed systems by an Artificial Bee Colony approach

Green Computing also known as Green IT is becoming a hot topic in the computational field during these last years. Green Computing consists of enabling organizations to make a more rational and efficient use of their technological resources and reduce costs while adopting technologies and working methods more respectful of the environment. Execution time and energy consumption are also conflicting objectives, because faster resources frequently imply higher energy consumptions. In this paper, we optimize both: execution time and energy consumption to resolve the task scheduling problem in Grid environments. MOABC is a swarm algorithm inspired in the bees behaviour and it is compared with MO-FA which is other swarm algorithm inspired in the fireflies behaviour. These algorithms are also compared with the well-known NSGA-II to evaluate their multiobjective properties. Moreover, the best algorithm, MOABC, is compared with MOHEFT, the most popular algorithm for workflow scheduling and with two real grid schedulers as WMS or DBC. The results obtained point out MOABC as the best approach in all the cases studied.