Energy-efficient scheduling algorithms based on task clustering in heterogeneous spark clusters

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Parallel Computing Pub Date : 2022-09-01 DOI:10.1016/j.parco.2022.102947

Wenhu Shi, Hongjian Li, Junzhe Guan, Hang Zeng, Rafe Misskat jahan

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引用次数: 1

Abstract

Spark is widely used for its fast in-memory processing. It is important to improve energy efficiency under deadline constrains. In this paper, a Task Performance Clustering of Best Fitting Decrease (TPCBFD) scheduling algorithm is proposed. It divides tasks in Spark into three types, with the different types of tasks being placed on nodes with superior performance. However, the basic computation time for TPCBFD takes up a large proportion of the task execution time, so the Energy-Aware TPCBFD (EATPCBFD) algorithm based on the proposed energy consumption model is proposed, focusing on optimizing energy efficiency and Service Level Agreement (SLA) service times. The experimental results show that EATPCBFD increases the average energy efficiency in Spark by 77% and the average passing rate of SLA service time by 14% compared to comparison algorithms. EATPCBFD has higher energy efficiency on average than comparison algorithms under deadline. The average energy efficiency of EATPCBFD with the deadline constraint is higher than the comparison algorithm.

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异构火花集群中基于任务聚类的节能调度算法

Spark因其快速的内存处理而被广泛使用。在期限限制下提高能源效率是很重要的。提出了一种任务性能聚类最优拟合递减调度算法(TPCBFD)。它将Spark中的任务分为三种类型，不同类型的任务被放置在性能优越的节点上。但由于TPCBFD的基本计算时间占任务执行时间的很大比例，因此提出了基于上述能耗模型的energy - aware TPCBFD (EATPCBFD)算法，该算法的重点是优化能源效率和SLA服务时间。实验结果表明，与比较算法相比，EATPCBFD在Spark中的平均能效提高了77%，SLA服务时间的平均合格率提高了14%。在截止日期下，EATPCBFD的平均能量效率高于比较算法。带deadline约束的EATPCBFD的平均能效高于比较算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications