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Optimizing Data Locality by Executor Allocation in Reduce Stage for Spark Framework

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成果类型:
会议论文
作者:
Fu, Zhongming;He, Mengsi;Tang, Zhuo;Zhang, Yang
作者机构:
[He, Mengsi; Fu, Zhongming] Univ South China, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Tang, Zhuo] Hunan Univ, Coll Informat Sci & Engn, Changsha, Peoples R China.
[Zhang, Yang] Natl Univ Def Technol, Sci & Technol Parallel & Distributed Lab PDL, Changsha, Peoples R China.
语种:
英文
关键词:
Communication distance;Data locality;Executor allocation;Spark
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2022
卷:
13148
页码:
349-357
会议名称:
22nd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)
会议论文集名称:
Lecture Notes in Computer Science
会议时间:
DEC 17-19, 2021
会议地点:
Sun Yat Sen Univ, Guangzhou, PEOPLES R CHINA
会议主办单位:
Sun Yat Sen Univ
主编:
Shen, H Sang, Y Zhang, Y Xiao, N Arabnia, HR Fox, G Gupta, A Malek, M
出版地:
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
出版者:
SPRINGER INTERNATIONAL PUBLISHING AG
ISBN:
978-3-030-96772-7; 978-3-030-96771-0
基金类别:
Doctoral Research Startup Foundation of University of South China [200XQD083]
机构署名:
本校为第一机构
院系归属:
计算机科学与技术学院
摘要:
Data locality is a key factor influencing the performance of Spark systems. As the execution container of tasks, the executors started on which nodes can directly affect the locality level achieved by the tasks. This paper tries to improve the data locality by executor allocation in reduce stage for Spark framework. Firstly, we calculate the network distance matrix of executors and formulate an optimal executor allocation problem to minimize the total communication distance. Then, an approximation algorithm is proposed and the approximate factor is proved to be 2. Finally, we evaluate the perf...

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