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Improving MapReduce performance in heterogeneous environments with adaptive task tuning

Authors:

Xiaobo ZhouAuthors Info & Claims

Middleware '14: Proceedings of the 15th International Middleware Conference

Pages 97 - 108

https://doi.org/10.1145/2663165.2666089

Published: 08 December 2014 Publication History

Abstract

The deployment of MapReduce in datacenters and clouds present several challenges in achieving good job performance. Compared to in-house dedicated clusters, datacenters and clouds often exhibit significant hardware and performance heterogeneity due to continuous server replacement and multi-tenant interferences. As most Mapreduce implementations assume homogeneous clusters, heterogeneity can cause significant load imbalance in task execution, leading to poor performance and low cluster utilizations. Despite existing optimizations on task scheduling and load balancing, MapReduce still performs poorly on heterogeneous clusters.

In this paper, we find that the homogeneous configuration of tasks on heterogeneous nodes can be an important source of load imbalance and thus cause poor performance. Tasks should be customized with different settings to match the capabilities of heterogeneous nodes. To this end, we propose an adaptive task tuning approach, Ant, that automatically finds the optimal settings for individual tasks running on different nodes. Ant works best for large jobs with multiple rounds of map task execution. It first configures tasks with randomly selected configurations and gradually improves tasks settings by reproducing the settings from best performing tasks and discarding poor performing configurations. To accelerate task tuning and avoid trapping in local optimum, Ant uses genetic functions during task configuration. Experimental results on a heterogeneous cluster and a virtual cluster with varying hardware capabilities show that Ant improves the average job completion time by 23%, 11%, and 16% compared to stock Hadoop, customized Hadoop with industry recommendations, and a profiling-based configuration approach, respectively.

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Cited By

Herodotou HKakoulli E(2023)Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage SystemsACM Transactions on Database Systems10.1145/362538948:4(1-40)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3625389
Lin YTang BZhou SXie ZYe B(2023)Efficient Node Selection for Coding-based Timely Computation over Heterogeneous Systems2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00065(246-253)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00065
Alamro SLan TSubramaniam S(2023)Forseti: Dynamic chunk-level reshaping for data processing on heterogeneous clustersJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.09.003171(14-23)Online publication date: Jan-2023
https://doi.org/10.1016/j.jpdc.2022.09.003
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Index Terms

Improving MapReduce performance in heterogeneous environments with adaptive task tuning
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Heterogeneous (hybrid) systems
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
      1. Software development methods

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Information & Contributors

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Published In

cover image ACM Conferences

Middleware '14: Proceedings of the 15th International Middleware Conference

December 2014

334 pages

ISBN:9781450327855

DOI:10.1145/2663165

General Chair:
Laurent Réveillère
LaBRI, University of Bordeaux, France
,
Program Chairs:
Lucy Cherkasova
HP Labs, USA
,
François Taïani
Université de Rennes 1 / IRISA, France

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 December 2014

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LaBRI
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Bordeaux
USENIX Assoc
GDR ASR
IBM
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Middleware '14: 15th International Middleware Conference

December 8 - 12, 2014

Bordeaux, France

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Middleware '14 Paper Acceptance Rate 27 of 144 submissions, 19%;

Overall Acceptance Rate 203 of 948 submissions, 21%

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Cited By

Herodotou HKakoulli E(2023)Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage SystemsACM Transactions on Database Systems10.1145/362538948:4(1-40)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3625389
Lin YTang BZhou SXie ZYe B(2023)Efficient Node Selection for Coding-based Timely Computation over Heterogeneous Systems2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00065(246-253)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00065
Alamro SLan TSubramaniam S(2023)Forseti: Dynamic chunk-level reshaping for data processing on heterogeneous clustersJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.09.003171(14-23)Online publication date: Jan-2023
https://doi.org/10.1016/j.jpdc.2022.09.003
Cha SLee MLee SOh HDwyer MDamian DZeller A(2022)SymTunerProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510185(2068-2079)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510185
Bei ZKim NHWang KYu Z(2022)OSC: An Online Self-Configuring Big Data Framework for Optimization of QoSIEEE Transactions on Computers10.1109/TC.2021.306327871:4(809-823)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TC.2021.3063278
Ajibade Lukuman Saheed Abu Bakar Kamalrulnizam Ahmed Aliyu Tasneem Darwish (2021)Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A ReviewSystematic Literature Review and Meta-Analysis Journal10.54480/slrm.v2i2.192:2(53-60)Online publication date: 19-Oct-2021
https://doi.org/10.54480/slrm.v2i2.19
Herodotou HKakoulli E(2021)TridentProceedings of the VLDB Endowment10.14778/3461535.346154514:9(1570-1582)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.14778/3461535.3461545
Bakni NAssayad I(2021)Survey on improving the performance of MapReduce in HadoopProceedings of the 4th International Conference on Networking, Information Systems & Security10.1145/3454127.3456617(1-5)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1145/3454127.3456617
Guo YShan HHuang SHwang KFan JYu Z(2021)GML: Efficiently Auto-Tuning Flink's Configurations Via Guided Machine LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.308160032:12(2921-2935)Online publication date: 1-Dec-2021
https://doi.org/10.1109/TPDS.2021.3081600
Zhong XLi MYang HLiu YQian D(2021)swMR: A Framework for Accelerating MapReduce Applications on Sunway TaihulightIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28812659:2(1020-1030)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TETC.2018.2881265
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