Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.

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Scheduling and resource allocation to optimize performance criteria in multi-cluster heterogeneous environments is known as an NP-hard problem, not only for the resource heterogeneity, but also for the possibility of applying co-allocation to take advantage of idle resources across clusters. A common practice is to use basic heuristics to attempt to optimize some performance criteria by treating the jobs in the waiting queue individually. More recent works proposed new optimization strategies based on Linear Programming techniques dealing with the scheduling of multiple jobs simultaneously. However, the time cost of these techniques makes them impractical for large-scale environments. Population-based meta-heuristics have proved their effectiveness for finding the optimal schedules in large-scale distributed environments with high resource diversification and large numbers of jobs in the batches. The algorithm proposed in the present work packages the jobs in the batch to obtain better optimization opportunities. It includes a multi-objective function to optimize not only the Makespan of the batches but also the Flowtime, thus ensuring a certain level of QoS from the users' point of view. The algorithm also incorporates heterogeneity and bandwidth awareness issues, and is useful for scheduling jobs in large-scale heterogeneous environments. The proposed meta-heuristic was evaluated with a real workload trace. The results show the effectiveness of the proposed method, providing solutions that improve the performance with respect to other well-known techniques in the literature.