A gradient-supported analysis of Pareto front in multi-objective extremal optimization-based processor load balancing

This paper concerns methodology for exploiting the multi-objective Extremal Optimization for load-balancing algorithms in high-performance distributed systems. In clusters and data centers, there has always been a trade-off between contradictory goals such as obtaining high performance, reducing inter-node communication, task or virtual machine migration, and energy savings. Thus, a multi-objective optimization strategy should be provided based on task migration to achieve an efficient processor load balance in the executive distributed environment, which is an NP-hard computational problem. The paper proposes a new selection scheme for the final load-balanced solution in the Pareto front. In this gradient-supported scheme, we examine lexicographic solutions relaxed by a margin of allowable loss, provided that the remaining optimization criteria are improved. This has been achieved by calculating the gradients of the tangent lines connecting the analyzed lexicographic solutions and the subsequent Pareto front points. The algorithm has been evaluated by comparative simulation experiments with application program graphs run in distributed systems. The evaluation, which included a comparison with a genetic algorithm, confirmed the very good performance of the proposed gradient-based Pareto front selection method.