AMOSA with analytical tuning parameters for heterogeneous computing scheduling problem

Abstract

In this paper, the analytical parameter tuning for the Archive Multi-objective Simulated Annealing (AMOSA) is described. The analytical tuning method yields the initial and final temperature, and the maximum metropolis length. The analytically tuned AMOSA is used to solve the Heterogeneous Computing Scheduling Problem with independent tasks and it is compared versus the AMOSA without parameter tuning. We approach this problem as multi-objective, considering the makespan and the energy consumption. Also, in the last years this problem has gained importance due to the energy awareness in high performance computing centers (HPCC). The hypervolume, generational distance, and spread metrics were used in order to measure the performance of the implemented algorithms.