research-article

Efficient process mapping in geo-distributed cloud data centers

Authors:

Amelie Chi Zhou,

Bingsheng He, and

Jidong ZhaiAuthors Info & Claims

SC '17: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2017

Article No.: 16, Pages 1 - 12

https://doi.org/10.1145/3126908.3126913

Published: 12 November 2017 Publication History

Abstract

Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways to map parallel processes to physical nodes (i.e., "process mapping") could significantly impact the performance of the applications because of non-uniform communication cost in such geo-distributed environments. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environments. In this paper, we propose a novel model to formulate the geo-distributed process mapping problem and develop a new method to efficiently find the near optimal solution. Our algorithm considers both the network communication performance of geo-distributed data centers as well as the communication matrix of the target application. Evaluation results with real experiments on Amazon EC2 and simulations demonstrate that our proposal achieves significant performance improvement (50% on average) compared to the state-of-the-art algorithms.

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cover image ACM Conferences

SC '17: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2017

801 pages

ISBN:9781450351140

DOI:10.1145/3126908

General Chair:
Bernd Mohr
Jülich Supercomputing Center, Jülich, Germany
,
Program Chair:
Padma Raghavan
Vanderbilt University, Nashville, TN

Copyright © 2017 ACM.

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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

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Published: 12 November 2017

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National Key Research and Development Program of China
Ministry of Education - Singapore
National Natural Science Foundation of China

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SC '17

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SIGHPC

SC '17: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 17, 2017

Colorado, Denver

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SC '17 Paper Acceptance Rate 61 of 327 submissions, 19%;

Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

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https://doi.org/10.1109/TCAD.2021.3068095
Mseddi ASalahuddin MZhani MElbiaze HGlitho R(2021)Efficient Replica Migration Scheme for Distributed Cloud Storage SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2018.28587929:1(155-167)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TCC.2018.2858792
Hao TZhan JHwang KGao WWen X(2021)AI-oriented Workload Allocation for Cloud-Edge Computing2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid51090.2021.00065(555-564)Online publication date: May-2021
https://doi.org/10.1109/CCGrid51090.2021.00065
Tang XWang HMa XEl-Sayed NZhai JChen WAboulnaga ATaufer MBalaji PPeña A(2019)Spread-n-shareProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3295500.3356152(1-15)Online publication date: 17-Nov-2019
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ZHOU AXiao Ygong yHe BZhai JMao R(2019)Privacy Regulation Aware Process Mapping in Geo-Distributed Cloud Data CentersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.2896894(1-1)Online publication date: 2019
https://doi.org/10.1109/TPDS.2019.2896894
Li WNiu DLiu YLiu SLi B(2019)Wide-Area Spark Streaming: Automated Routing and Batch SizingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.288018930:6(1434-1448)Online publication date: 1-Jun-2019
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Chen SWang WRodero I(2019)Exploring the Potential of Elastic Computing Clusters in Geo-Distributed Data Centers with Fast Fabric Interconnection2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00135(937-944)Online publication date: Aug-2019
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