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Towards practical and near-optimal coflow scheduling for data center networks

Luo, Shouxi, Yu, Hongfang, Zhao, Yangming, Wang, Sheng, Yu, Shui and Li, Lemin 2016, Towards practical and near-optimal coflow scheduling for data center networks, IEEE transactions on parallel and distributed systems, vol. 27, no. 11, pp. 3366-3380, doi: 10.1109/TPDS.2016.2525767.

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Title Towards practical and near-optimal coflow scheduling for data center networks
Author(s) Luo, Shouxi
Yu, Hongfang
Zhao, Yangming
Wang, Sheng
Yu, ShuiORCID iD for Yu, Shui
Li, Lemin
Journal name IEEE transactions on parallel and distributed systems
Volume number 27
Issue number 11
Start page 3366
End page 3380
Total pages 15
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-11-01
ISSN 1045-9219
Keyword(s) schedules
job shop scheduling
processor scheduling
real time systems
datacenter networks
Summary In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.
Language eng
DOI 10.1109/TPDS.2016.2525767
Field of Research 080309 Software Engineering
080599 Distributed Computing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE.
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Document type: Journal Article
Collection: School of Information Technology
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Created: Tue, 29 Nov 2016, 09:44:18 EST

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