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On the core affinity and file upload performance of Hadoop

Authors:

Joong-Yeon Cho,

Karsten SchwanAuthors Info & Claims

DISCS-2013: Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems

Pages 25 - 30

https://doi.org/10.1145/2534645.2534651

Published: 18 November 2013 Publication History

Abstract

The MapReduce programming model is introduced for big-data processing, where the data nodes perform both data storing and computation. Thus, we need to understand different resource requirements of data storing and computation tasks and schedule these efficiently over multi-core processors. The core affinity defines mapping between a set of cores and a given task. The core affinity can be decided based on resource requirements of a task because this largely affects the efficiency of computation, memory, and I/O resource utilization. In this paper, we analyze the impact of core affinity on the file upload performance of Hadoop Distributed File System (HDFS). Our study can provide the insight into the process scheduling issues on big-data processing systems. We also suggest a framework for dynamic core affinity based on our observations and show that a preliminary implementation can improve the throughput more than 40% compared with default Linux system.

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Cited By

Hanford NAhuja VFarrens MTierney BGhosal D(2018)A Survey of End-System Optimizations for High-Speed NetworksACM Computing Surveys10.1145/318489951:3(1-36)Online publication date: 16-Jul-2018
https://dl.acm.org/doi/10.1145/3184899
Cho JJin HLee MSchwan K(2014)Dynamic core affinity for high-performance file upload on Hadoop Distributed File SystemParallel Computing10.1016/j.parco.2014.07.00540:10(722-737)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.parco.2014.07.005

Index Terms

On the core affinity and file upload performance of Hadoop
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management
        Scheduling

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Information & Contributors

Information

Published In

cover image ACM Conferences

DISCS-2013: Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems

November 2013

66 pages

ISBN:9781450325066

DOI:10.1145/2534645

General Chair:
Xian-He Sun
Illinois Institute of Technology
,
Program Chairs:
Yong Chen
Texas Tech University
,
Philip C. Roth
Oak Ridge National Laboratory

Copyright © 2013 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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New York, NY, United States

Publication History

Published: 18 November 2013

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National Research Foundation of Korea

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SC13

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 18, 2013

Colorado, Denver

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DISCS-2013 Paper Acceptance Rate 10 of 19 submissions, 53%;

Overall Acceptance Rate 19 of 34 submissions, 56%

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Cited By

Hanford NAhuja VFarrens MTierney BGhosal D(2018)A Survey of End-System Optimizations for High-Speed NetworksACM Computing Surveys10.1145/318489951:3(1-36)Online publication date: 16-Jul-2018
https://dl.acm.org/doi/10.1145/3184899
Cho JJin HLee MSchwan K(2014)Dynamic core affinity for high-performance file upload on Hadoop Distributed File SystemParallel Computing10.1016/j.parco.2014.07.00540:10(722-737)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.parco.2014.07.005

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