【摘 要】
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Recently,a growing number of scientific applica-tions have been migrated into the cloud.To deal with the prob-lems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow scheduling.However,the previous works
【机 构】
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School of Computer Science and Technology,Shandong University,Jinan 250101,China;Shanghai Police Col
论文部分内容阅读
Recently,a growing number of scientific applica-tions have been migrated into the cloud.To deal with the prob-lems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow scheduling.However,the previous works ignore some details,which are challenging but essential.Most existing multi-objective work-flow scheduling algorithms overlook weight selection,which may result in the quality degradation of solutions.Besides,we find that the famous partial critical path (PCP) strategy,which has been widely used to meet the deadline constraint,can not accurately reflect the situation of each time step.Work-flow scheduling is an NP-hard problem,so self-optimizing al-gorithms are more suitable to solve it.In this paper,the aim is to solve a workflow scheduling problem with a deadline constraint.We design a deadline con-strained scientific workflow scheduling algorithm based on multi-objective reinforcement learning (RL) called DCMORL.DCMORL uses the Chebyshev scalarization function to scalar-ize its Q-values.This method is good at choosing weights for objectives.We propose an improved version of the PCP strat-egy called MPCP.The sub-deadlines in MPCP regularly update during the scheduling phase,so they can accurately reflect the situation of each time step.The optimization objectives in this paper include minimizing the execution cost and energy con-sumption within a given deadline.Finally,we use four scien-tific workflows to compare DCMORL and several representa-tive scheduling algorithms.The results indicate that DCMORL outperforms the above algorithms.As far as we know,it is the first time to apply RL to a deadline constrained workflow scheduling problem.
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