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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters
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EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

机译：Eatsdcd：使用云数据中心中的群集，复制和DVFS技术的基于并行任务的应用程序的绿色能量感知调度算法

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摘要

Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with En(cur) = 1.2En(min)(G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.
机译：能量消耗和性能指标已成为在云数据中心等高性能计算系统中调度并行任务的应用程序的关键问题。复制和聚类策略以及动态电压频率缩放（DVFS）技术分别集中在减少能耗和优化吞吐量和Makespan等性能参数上。本文提出了一种称为EATSDCD的双相算法，该算法是一种能量有效时间所知。该算法使用重复和聚类策略的组合来通过DVFS将数据中心处理器上的优先级受约束的任务图进行调度。第一阶段侧重于复制和聚类策略的智能组合，以减少处理器消耗的薄型和能量，以便在满足吞吐量约束的同时执行定向的非循环图（DAG）。 Eatsdcd背后的主要思想旨在最大限度地减少第二阶段的能耗。在确定关键路径并在非关键路径中指定一组从属任务之后，在该路径中的所有相关任务中分发了非关键路径中的每个任务的松弛时间。然后，缩小DVFS的处理器的频率以执行非关键任务以及空闲和通信阶段，而无需扩展任务的执行时间。最后，开发了一个试验台，在随机产生的DAG上测试了不同的参数，以评估并说明Eatsdcd的有效性。还与基于重复和基于聚类的算法和基于DVFS的算法进行了比较。在能量消耗和MEPESPAN方面，结果表明，与基于动力感知列表的调度（PALS）和功率感知任务聚类（PATC）算法相比，我们所提出的算法可以节省高达8.3％和20％的能量。此外，平行管道延迟优化（Papilo）算法有16％的改善（Cur）= 1.2EN（min）（g）。与具有复制（RASD）算法的可靠性意识调度相比，异构环境中的执行时间已经减少。

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