Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107281
Title: Towards intelligent datacenter traffic management : using automated fuzzy inferencing for elephant flow detection
Authors: Pham, Manh Tung
Seow, Kiam Tian
Foh, Chuan Heng
Keywords: DRNTU::Engineering::Computer science and engineering::Information systems
Issue Date: 2014
Source: Pham, M. T., Seow, K. T., & Foh, C. H. (2014). Towards intelligent datacenter traffic management : using automated fuzzy inferencing for elephant flow detection. International journal of innovative computing, information and control, 10(5 ), 1669-1685.
Series/Report no.: International journal of innovative computing, information and control
Abstract: Effective traffic management has always been one of the key considerations in datacenter design. It plays an even more important role today in the face of increasingly widespread deployment of communication intensive applications and cloud- based services, as well as the adoption of multipath datacenter topologies to cope with the enormous bandwidth requirements arising from those applications and services. Of central importance in traffic management for multipath datacenters is the problem of timely detection of elephant flows flows that carry huge amount of data so that the best paths can be selected for these flows, which otherwise might cause serious network congestion. In this paper, we propose FuzzyDetec, a novel control architecture for the adaptive detection of elephant flows in multipath datacenters based on fuzzy logic. We develop, perhaps for the first time, a close loop elephant flow detection framework with an automated fuzzy inference module that can continually compute an appropriate threshold for elephant flow detection based on current information feedback from the network. The novelty and practical significance of the idea lie in allowing multiple imprecise and possibly conflicting criteria to be incorporated into the elephant flow detection process, through simple fuzzy rules emulating human expertise in elephant flow threshold classification. The proposed approach is simple, intuitive and easily extensible, providing a promising direction towards intelligent datacenter traffic management for autonomous high performance datacenter networks. Simulation results show that, in comparison with an existing state-of-the-art elephant flow detection framework, our proposed approach can provide considerable throughput improvements in datacenter network routing.
URI: https://hdl.handle.net/10356/107281
http://hdl.handle.net/10220/25533
Rights: © 2014 ICIC International. This paper was published in International Journal of Innovative Computing, Information and Control and is made available as an electronic reprint (preprint) with permission of ICIC International. The paper can be found at the following official URL: [http://www.ijicic.org/contents.htm]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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