Information-agnostic coflow scheduling with optimal demotion thresholds

Gao, Yuanxiang, Yu, Hongfang, Luo, Shouxi and Yu, Shui 2016, Information-agnostic coflow scheduling with optimal demotion thresholds, in ICC 2016 : Proceedings of the 2016 IEEE International Conference on Communications, IEEE, Piscataway, N. J., pp. 1-6, doi: 10.1109/ICC.2016.7511241.

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Title Information-agnostic coflow scheduling with optimal demotion thresholds
Author(s) Gao, Yuanxiang
Yu, Hongfang
Luo, Shouxi
Yu, ShuiORCID iD for Yu, Shui
Conference name IEEE Communications. Conference (2016 : Kuala Lumper, Malaysia)
Conference location Kuala Lumpur, Malaysia
Conference dates 22-27 May. 2016
Title of proceedings ICC 2016 : Proceedings of the 2016 IEEE International Conference on Communications
Editor(s) [Unknown]
Publication date 2016
Conference series IEEE International Conference on Communications
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) Delays
Algorithm design and analysis
queueing analysis
design standards
analytic models
Summary Previous coflow scheduling proposals improve the coflow completion time (CCT) over per-flow scheduling based on prior information of coflows, which makes them hard to apply in practice. State-of-art information-agnostic coflow scheduling solution Aalo adopts Discretized Coflow-aware Least-Attained-Service (D-CLAS) to gradually demote coflows from the highest priority class into several lower priority classes when their sent-bytes-count exceeds several predefined demotion thresholds. However, current design standards of these demotion thresholds are crude because they do not analyze the impacts of different demotion thresholds on the average coflow delay. In this paper, we model the D-CLAS system by an M/G/1 queue and formulate the average coflow delay as a function of the demotion thresholds. In addition, we prove the valley-like shape of the function and design the Down-hill searching (DHS) algorithm. The DHS algorithm locates a set of optimal demotion thresholds which minimizes the average coflow delay in the system. Real-data-center-trace driven simulations indicate that DHS improves average CCT up to 6.20× over Aalo.
ISBN 9781479966646
Language eng
DOI 10.1109/ICC.2016.7511241
Field of Research 080109 Pattern Recognition and Data Mining
080503 Networking and Communications
100699 Computer Hardware not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
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