TY - JOUR
T1 - Intelligently modeling, detecting, and scheduling elephant flows in software defined energy cloud
T2 - A survey
AU - Liao, Ling Xia
AU - Chao, Han Chieh
AU - Chen, Mu Yen
N1 - Funding Information:
Dr. Han-Chieh Chao is a Full Professor and Chair of the Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C. His research interests include High Speed Networks, Wireless Networks, IPv6 based Networks and Digital Divide. He received his MS and Ph.D. degrees in Electrical Engineering from Purdue University in 1989 and 1993 respectively. He has authored or co-authored 3 books and has published about 100 refereed professional research papers. He has completed 28 MSEE thesis students. Dr. Chao has received many research awards, including Purdue University SRC awards, and NSC research awards (National Science Council of Taiwan). He also received many funded research grants from NSC, Ministry of Education (MOE), RDEC, Industrial Technology of Research Institute, Institute of Information Industry and FarEasTone Telecommunications Lab. Dr. Chao has been invited frequently to give talks at national and international conferences and research organizations. Dr. Chao is also serving as an IPv6 Steering Committee member and co-chair of R & D division of the NICI (National Information and Communication Initiative, a ministry level government agency which aims to integrate domestic IT and Telecom projects of Taiwan), Co-chair of the Technical Area for IPv6 Forum Taiwan, the executive editor of the Journal of Internet Technology and the Editor-in-Chief for International Journal of Internet Protocol Technology and International Journal of Ad Hoc and Ubiquitous Computing. Dr. Chao is an IEEE senior member.
Funding Information:
This work was supported by funding from the National Natural Science Foundation of China (Grant No. 61962016 ).
Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/12
Y1 - 2020/12
N2 - Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized, and (2) dynamic models and advanced policies are difficult to be applied. Software Defined Cloud (SDC) addresses these issues by enabling controller-based approaches. With the aid of Machine Learning (ML) technologies, SDC can achieve learning-based models, flexible deployment, and early detection and schedule of elephants for the optimization of network performance and energy usage in a dynamic and intelligent manner. On this purpose, this article emphases the significance of models describing elephants, surveys the mechanisms that may apply to model, detect, and schedule elephants for SDC to optimize the network performance and energy usage. To the best of our knowledge, this work is the first effort that reviews the techniques in all these related subtopics simultaneously in the context of energy cloud.
AB - Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized, and (2) dynamic models and advanced policies are difficult to be applied. Software Defined Cloud (SDC) addresses these issues by enabling controller-based approaches. With the aid of Machine Learning (ML) technologies, SDC can achieve learning-based models, flexible deployment, and early detection and schedule of elephants for the optimization of network performance and energy usage in a dynamic and intelligent manner. On this purpose, this article emphases the significance of models describing elephants, surveys the mechanisms that may apply to model, detect, and schedule elephants for SDC to optimize the network performance and energy usage. To the best of our knowledge, this work is the first effort that reviews the techniques in all these related subtopics simultaneously in the context of energy cloud.
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U2 - 10.1016/j.jpdc.2020.07.008
DO - 10.1016/j.jpdc.2020.07.008
M3 - Article
AN - SCOPUS:85089484705
VL - 146
SP - 64
EP - 78
JO - Journal of Parallel and Distributed Computing
JF - Journal of Parallel and Distributed Computing
SN - 0743-7315
ER -