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TASK SCHEDULING ALGORITHM FOR HETEROGENEOUS MULTI PROCESSING COMPUTING SYSTEMS

机译:异构多处理计算系统的任务调度算法

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The main purpose of task scheduling is to assign tasks onto available processors with the aim of producing minimum schedule length and without violating the precedence constraints. In heterogeneous multi-processing systems, task assignments and scheduling have a great impact on the system operation. In a heuristic based task scheduling algorithm, different process will result different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be capable to efficiently assign precedence to each subtask depending on the resources required to reduce makespan. In this report, we propose a genetic algorithm (PGA) to resolve a task assignment and scheduling for homogeneous and heterogeneous multi-processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to decrease space search and the time needed to get the best solution. The achieved results show that the suggested approach significantly outperforms the other approaches in terms of task execution time.
机译:任务调度的主要目的是将任务分配到可用处理器上,以产生最小的调度长度,而又不违反优先级约束。在异构多处理系统中,任务分配和调度对系统操作有很大影响。在基于启发式的任务调度算法中,不同的过程将在异构计算系统上导致不同的任务执行时间(makespan)。因此,良好的调度算法应能够根据减少有效期所需的资源为每个子任务有效地分配优先级。在此报告中,我们提出了一种遗传算法(PGA),以解决同类和异构多处理问题的任务分配和调度。此过程的基本思想是利用基于启发式算法的优势来减少空间搜索,并减少获得最佳解决方案所需的时间。取得的结果表明,在任务执行时间方面,建议的方法明显优于其他方法。

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