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Robust static allocation of resources for independent tasks under makespan and dollar cost constraints

机译:在制造期和美元成本约束下,针对独立任务的可靠静态分配资源

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Heterogeneous computing (HC) systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be inaccuracies in the estimation of task execution times. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that needs to be optimized in such systems. Resource allocation is typically performed based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. In this research, the problem of finding a static mapping of tasks to maximize the robustness of makespan against the errors in task execution time estimates given an overall makespan constraint is studied. Two variations of this basic problem are considered: (1) where there is a given, fixed set of machines, (2) where an HC system is to be constructed from a set of machines within a dollar cost constraint. Six heuristic techniques for each of these variations of the problem are presented and evaluated.
机译:由具有不同计算能力的相互连接的机器组成的异构计算(HC)系统通常在任务执行时间的估计可能不准确的环境中运行。 Makespan(定义为整套任务的完成时间)通常是在此类系统中需要优化的性能功能。通常基于对每类机器上每个任务的计算时间的估计来执行资源分配。因此,重要的是使makepan能够抵抗计算时间估计中的错误。在这项研究中,研究了在给定整体制造期约束的情况下,找到任务的静态映射以最大化制造期的鲁棒性,以克服任务执行时间估计中的错误的问题。考虑了此基本问题的两个变体:(1)有一组给定的固定机器;(2)由一台机器构成的HC系统在美元成本的约束下。针对问题的这些变化中的每一个,提出并评估了六种启发式技术。

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