Improving Data Locality of Tasks by Executor Allocation in Spark Computing Environment

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhongming Fu;Mengsi He;Yang Yi;Zhuo Tang
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引用次数: 0

Abstract

The concept of data locality is crucial for distributed systems (e.g., Spark and Hadoop) to process Big Data. Most of the existing research optimized the data locality from the aspect of task scheduling. However, as the execution container of Spark's tasks, the executor launched on different nodes can directly affect the data locality achieved by the tasks. This article tries to improve the data locality of tasks by executor allocation in Spark framework. First, because of different communication modes at stages, we separately model the communication cost of tasks for transferring input data to the executors. Then formalize an optimal executor allocation problem to minimize the total communication cost of transferring all input data. This problem is proven to be NP-hard. Finally, we present a greed dropping heuristic algorithm to provide solution to the executor allocation problem. Our proposals are implemented in Spark-3.4.0 and its performance is evaluated through representative micro-benchmarks (i.e., WordCount , Join , Sort ) and macro-benchmarks (i.e., PageRank and LDA ). Extensive experiments show that the proposed executor allocation strategy can decrease the network traffic and data access time by improving the data locality during the task scheduling. Its performance benefits are particularly significant for iterative applications.
通过 Spark 计算环境中的执行器分配提高任务的数据位置性
数据局部性的概念对于分布式系统(如 Spark 和 Hadoop)处理大数据至关重要。现有研究大多从任务调度方面优化数据本地性。然而,作为 Spark 任务的执行容器,不同节点上启动的执行器会直接影响任务实现的数据局部性。本文试图通过 Spark 框架中的执行器分配来改善任务的数据局部性。首先,由于各阶段的通信模式不同,我们分别建立了任务向执行器传输输入数据的通信成本模型。然后形式化一个最优执行器分配问题,以最小化传输所有输入数据的总通信成本。这个问题被证明是 NP 难的。最后,我们提出了一种放弃贪婪的启发式算法,为执行器分配问题提供了解决方案。我们的建议在 Spark-3.4.0 中实现,并通过代表性的微基准(即 WordCount、Join、Sort)和宏基准(即 PageRank 和 LDA)对其性能进行了评估。广泛的实验表明,所提出的执行器分配策略可以在任务调度过程中改善数据位置,从而减少网络流量和数据访问时间。其性能优势对于迭代应用尤为显著。
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
IEEE Transactions on Cloud Computing
IEEE Transactions on Cloud Computing Computer Science-Software
CiteScore
9.40
自引率
6.20%
发文量
167
期刊介绍: The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.
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