Time optimization modeling for big data placement and analysis for geo-distributed data centers

Awais Khan; Muhammad Attique; Tae Sun Chung; Youngjae Kim

doi:10.1109/CLUSTER.2016.75

Time optimization modeling for big data placement and analysis for geo-distributed data centers

Awais Khan, Muhammad Attique, Tae Sun Chung, Youngjae Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Big data storage and sharing are becoming the major demand of the community. To overcome such issues, virtually unified data facilities are being presented with geodistributed data centers by providing the user with the single unified namespace. These unified data storage facilities lack efficient storage and analysis of data. To address these shortcomings in such unified data facilities, we designed and implemented time optimization model which minimizes the job execution time whereby selecting optimal data center with constraints of storage, computational and network bandwidth among all data centers. Our extensive simulation results show that our model provides the optimal decision that leads to minimal end-to-end data placement and analysis times.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	140-141
Number of pages	2
ISBN (Electronic)	9781509036530
DOIs	https://doi.org/10.1109/CLUSTER.2016.75
State	Published - Dec 6 2016
Externally published	Yes
Event	2016 IEEE International Conference on Cluster Computing, CLUSTER 2016 - Taipei, Taiwan, Province of China Duration: Sep 13 2016 → Sep 15 2016

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)	1552-5244

Conference

Conference	2016 IEEE International Conference on Cluster Computing, CLUSTER 2016
Country/Territory	Taiwan, Province of China
City	Taipei
Period	09/13/16 → 09/15/16

Funding

This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant (No. R0190- 15-2012) and National Research Foundation of Korea (NRF) grant (No. 2015R1C1A1A0152105), and partially by the Ajou University research fund.

Funders	Funder number
Ajou University
National Research Foundation of Korea	2015R1C1A1A0152105
Institute for Information and Communications Technology Promotion	R0190- 15-2012

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10.1109/CLUSTER.2016.75

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Khan, A., Attique, M., Chung, T. S., & Kim, Y. (2016). Time optimization modeling for big data placement and analysis for geo-distributed data centers. In Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016 (pp. 140-141). Article 7776491 (Proceedings - IEEE International Conference on Cluster Computing, ICCC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2016.75

Khan, Awais ; Attique, Muhammad ; Chung, Tae Sun et al. / Time optimization modeling for big data placement and analysis for geo-distributed data centers. Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 140-141 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

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title = "Time optimization modeling for big data placement and analysis for geo-distributed data centers",

abstract = "Big data storage and sharing are becoming the major demand of the community. To overcome such issues, virtually unified data facilities are being presented with geodistributed data centers by providing the user with the single unified namespace. These unified data storage facilities lack efficient storage and analysis of data. To address these shortcomings in such unified data facilities, we designed and implemented time optimization model which minimizes the job execution time whereby selecting optimal data center with constraints of storage, computational and network bandwidth among all data centers. Our extensive simulation results show that our model provides the optimal decision that leads to minimal end-to-end data placement and analysis times.",

author = "Awais Khan and Muhammad Attique and Chung, {Tae Sun} and Youngjae Kim",

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year = "2016",

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doi = "10.1109/CLUSTER.2016.75",

language = "English",

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Khan, A, Attique, M, Chung, TS & Kim, Y 2016, Time optimization modeling for big data placement and analysis for geo-distributed data centers. in Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016., 7776491, Proceedings - IEEE International Conference on Cluster Computing, ICCC, Institute of Electrical and Electronics Engineers Inc., pp. 140-141, 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016, Taipei, Taiwan, Province of China, 09/13/16. https://doi.org/10.1109/CLUSTER.2016.75

Time optimization modeling for big data placement and analysis for geo-distributed data centers. / Khan, Awais; Attique, Muhammad; Chung, Tae Sun et al.
Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 140-141 7776491 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Big data storage and sharing are becoming the major demand of the community. To overcome such issues, virtually unified data facilities are being presented with geodistributed data centers by providing the user with the single unified namespace. These unified data storage facilities lack efficient storage and analysis of data. To address these shortcomings in such unified data facilities, we designed and implemented time optimization model which minimizes the job execution time whereby selecting optimal data center with constraints of storage, computational and network bandwidth among all data centers. Our extensive simulation results show that our model provides the optimal decision that leads to minimal end-to-end data placement and analysis times.

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M3 - Conference contribution

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Khan A, Attique M, Chung TS, Kim Y. Time optimization modeling for big data placement and analysis for geo-distributed data centers. In Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 140-141. 7776491. (Proceedings - IEEE International Conference on Cluster Computing, ICCC). doi: 10.1109/CLUSTER.2016.75

Time optimization modeling for big data placement and analysis for geo-distributed data centers

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