Online Runtime Prediction Method for Distributed Iterative Jobs

Xiaofei Yue, Lan Shi, Yuhai Zhao*, Hangxu Ji, Guoren Wang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Predicting the runtime of distributed iterative jobs can help reduce the deployment cost of clusters and optimize their resource allocation and scheduling strategies, but the runtime depends on various factors which are difficult to be acquired before execution. In this paper, we propose a generalized online prediction method for the runtime of distributed iterative jobs, which is centered on a series of online machine learning models. The method consists of three phases: 1) estimating the number of iterations for the current iterative job. 2) predicting the runtime metrics of each iteration by an online polynomial regression model. 3) Runtime metrics sequence is analyzed using an LSTM trained with online learning to predict the runtime of each iteration. We conducted experiments on typical Flink iterative jobs, and the experimental results show that our method improves the accuracy by 4.79% compared to the state-of-the-art methods, while for the improvement in accuracy for delta iterative jobs is even more than 15%.

源语言英语
主期刊名Web Information Systems and Applications - 18th International Conference, WISA 2021, Proceedings
编辑Chunxiao Xing, Xiaoming Fu, Yong Zhang, Guigang Zhang, Chaolemen Borjigin
出版商Springer Science and Business Media Deutschland GmbH
156-168
页数13
ISBN(印刷版)9783030875701
DOI
出版状态已出版 - 2021
活动18th International Conference on Web Information Systems and Applications, WISA 2021 - Kaifeng, 中国
期限: 24 9月 202126 9月 2021

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12999 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议18th International Conference on Web Information Systems and Applications, WISA 2021
国家/地区中国
Kaifeng
时期24/09/2126/09/21

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