Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy-conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy-conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy-conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real-world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy-saving and makespan reducing.

中文翻译：

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在分布式异构计算系统中调度节能任务

分布式异构系统通过提供高可扩展性和性能，同时控制复杂性和能耗，在工业应用中得到广泛采用。然而，随着计算节点数量的增加，分布式异构系统的能耗急剧增长，并且极其难以预测。节能任务调度试图为任务分配适当的优先级和处理器以满足系统能量需求，近年来受到广泛关注。然而，许多方法通过延长完成时间来降低能耗。在这篇文章中，我们专注于分布式异构计算系统中节能任务的调度问题，并提供一种有效的方法来减轻能耗，同时最大限度地减少并行应用程序的整体完工时间。首先，基于异构最早完成时间，提出了一个适应度函数来平衡完工时间和能量消耗。然后，通过改进传统遗传算法的交叉和变异操作，我们提出了一种名为能量意识遗传算法的高效调度方法，以优化任务的优先级和处理器分配，目标是最小化系统能量和完工时间。