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Scheduling Precedence Constrained Tasks with Reduced Processor Energy on Multiprocessor Computers

机译:在多处理器计算机上以较低的处理器能量调度优先约束任务

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Energy-efficient scheduling of sequential tasks with precedence constraints on multiprocessor computers with dynamically variable voltage and speed is investigated as combinatorial optimization problems. In particular, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint are considered. Our scheduling problems contain three nontrivial subproblems, namely, precedence constraining, task scheduling, and power supplying. Each subproblem should be solved efficiently so that heuristic algorithms with overall good performance can be developed. Such decomposition of our optimization problems into three subproblems makes design and analysis of heuristic algorithms tractable. Three types of heuristic power allocation and scheduling algorithms are proposed for precedence constrained sequential tasks with energy and time constraints, namely, prepower-determination algorithms, postpower-determination algorithms, and hybrid algorithms. The performance of our algorithms are analyzed and compared with optimal schedules analytically. Such analysis has not been conducted in the literature for any algorithm. Therefore, our investigation in this paper makes initial contribution to analytical performance study of heuristic power allocation and scheduling algorithms for precedence constrained sequential tasks. Our extensive simulation data demonstrate that for wide task graphs, the performance ratios of all our heuristic algorithms approach one as the number of tasks increases.
机译:在具有动态可变电压和速度的多处理器计算机上,具有优先约束的顺序任务的节能调度被研究为组合优化问题。特别地,考虑了具有能量消耗约束的最小调度长度的问题和具有能量消耗约束的最小能量消耗的问题。我们的调度问题包含三个非平凡的子问题,即优先约束,任务调度和供电。应该有效地解决每个子问题,以便可以开发总体性能良好的启发式算法。将优化问题分解为三个子问题,使启发式算法的设计和分析变得易于处理。针对具有能量和时间约束的优先约束顺序任务,提出了三种启发式功率分配和调度算法,即预功率确定算法,后功率确定算法和混合算法。分析了我们算法的性能,并与最佳计划进行了分析比较。文献中没有针对任何算法进行这种分析。因此,本文的研究为优先约束顺序任务的启发式功率分配和调度算法的分析性能研究做出了初步贡献。我们广泛的仿真数据表明,对于宽范围的任务图,随着任务数量的增加,我们所有启发式算法的性能比都接近1。

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