Grid scheduling is one of the most known NP-complete problems. Heterogeneity of machines causes mapping of tasks to be a challenging problem. Several meta-heuristic algorithms have been designed to reach optimality as possible. Sort-Mid is a recent efficient scheduler of excellent resource utilization. Its strategy was based on computing the mean of two consecutive middle values in the sorted list of completion time. Thereafter, it maps the task with the maximum mean to the machine having the minimum completion time. However, Range-Suffrage scheduler obtains a better makespan. It was built on searching for the maximum average value of completion times among certain tasks. These tasks were selected according to their suffrage values under specified constraint. This paper proposes RSSM as highly efficient scheduler. It combines Sort-Mid and Range-Suffrage algorithms to achieve the maximum resource utilization and the minimum makespan. RSSM methodology is based on sorting only the completion time corresponding to tasks satisfying Range-Suffrage constraint. Experimental tests manifest the superiority of proposed algorithm over the two original meta- heuristics and other promising algorithms such as Min-Min.

网格调度是最著名的 NP 完全问题之一。机器的异构性导致任务映射成为一个具有挑战性的问题。已经设计了几种元启发式算法以尽可能达到最优。Sort-Mid 是最近的一个优秀资源利用率的高效调度器。它的策略基于计算完成时间排序列表中两个连续中间值的平均值。此后，它将具有最大平均值的任务映射到具有最小完成时间的机器。然而，Range-Suffrage 调度程序获得了更好的完工时间。它建立在搜索某些任务中完成时间的最大值的基础上。这些任务是根据他们在指定约束下的选举权值来选择的。本文提出 RSSM 作为高效调度器。它结合了 Sort-Mid 和 Range-Suffrage 算法，以实现最大的资源利用率和最小的完工时间。RSSM 方法基于仅对满足 Range-Suffrage 约束的任务对应的完成时间进行排序。实验测试表明，所提出的算法优于两种原始元启发式算法和其他有前途的算法，例如 Min-Min。