Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm

Most of the approaches to achieve exascale computing heavily rely on designing power efficient hardware, but experts usually forget that the usage of efficient middlewares, like resource managers or job schedulers, can also play an important role in optimizing power and performance of supercomputing infrastructures. For the optimization of both, power and performance, we propose the implementation of a multiobjective version of artificial bee colony algorithm (MOABC). We have compared our algorithm with other deterministic (first-fit and MOHEFT) and stochastic (NSGA-II) resource selection approaches. The results of our simulations show that, in real computing environments, MOABC is more likely to obtain better optimizations of response times and power consumption.