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首页> 外文期刊>Current Journal of Applied Science and Technology >An Optimized Genetic Approach for SchedulingTask Duplication in Parallel Systems
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An Optimized Genetic Approach for SchedulingTask Duplication in Parallel Systems

机译：并行系统中任务复制的优化遗传方法

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Task Scheduling deals with the set of tasks assigned to parallel multiprocessor system and the execution order of the schedule so that the total execution time is minimized. The role of a good scheduling algorithm is to efficiently assign each task to a processor depending on the resources needed, the communication overhead between related tasks is reduced and the precedence relations among tasks are satisfied. It can be efficiently used for tasks that have a large calculation, and have time constraints to complete the schedule. The efficient execution of the task scheduling on parallel system takes the structure of the task and the performance characteristics of the proposed genetic algorithm. It falls in the category of NP-complete problem. This study proposes a parallel genetic algorithm-based approach to schedule tasks on parallel system with task duplication heuristics. Task duplication can minimize inter-processor communication and hence results in shorter finish times. Its performance is measured in comparison with the Round Robin (RR), First Come First Serve (FCFS), and Multi-level queue scheduling (MQS), Shortest Job First (SJF), Largest Job First (LJF) and Priority scheduling methods.
机译：任务计划处理分配给并行多处理器系统的任务集和计划的执行顺序，从而使总执行时间最小化。好的调度算法的作用是根据所需的资源有效地将每个任务分配给处理器，减少相关任务之间的通信开销，并满足任务之间的优先级关系。它可以有效地用于计算量大并且有时间限制以完成计划的任务。并行系统上任务调度的有效执行需要任务的结构以及所提出遗传算法的性能特征。它属于NP-完全问题。这项研究提出了一种基于并行遗传算法的方法，可以在任务重复启发式的并行系统上调度任务。任务重复可以最大程度地减少处理器间的通信，从而缩短完成时间。它的性能是与Round Robin（RR），先来先服务（FCFS）和多级队列调度（MQS），最短作业优先（SJF），最大作业优先（LJF）和优先级调度方法进行比较的。

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