AggreFlow: Achieving Power Efficiency, Load Balancing, and Quality of Service in Data Center Networks
Guo, Zehua1; Xu, Yang2,3; Liu, Ya-Feng4; Liu, Sen2; Chao, H. Jonathan5; Zhang, Zhi-Li6; Xia, Yuanqing1
AbstractPower-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing, Lazy Rerouting, and Adaptive Rerouting. Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow's performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes.
KeywordLoad management Control systems Quality of service Data centers Power demand Dynamic scheduling Flow scheduling power-efficient data center networks power saving OpenFlow
Indexed BySCI
Funding ProjectNational Key Research and Development Program of China[2018YFB1003700] ; Natural Science Foundation of China[62002019] ; Natural Science Foundation of China[11688101] ; Natural Science Foundation of China[11671419] ; Natural Science Foundation of China[11991021] ; Natural Science Foundation of China[62002066] ; Beijing Institute of Technology Research Fund Program for Young Scholars ; Project PCL Future GreaterBay Area Network Facilities for Large-scale Experiments and Applications[LZC0019] ; U.S. NSF[CNS-1618339] ; U.S. NSF[CNS-1617729] ; U.S. NSF[CNS-1814322]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000619370600002
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Document Type期刊论文
Corresponding AuthorXu, Yang
Affiliation1.Beijing Inst Technol, Beijing 100081, Peoples R China
2.Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China
3.Peng Cheng Lab, Shenzhen 518066, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math & Sci Engn Comp, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China
5.NYU, Tandon Sch Engn, Brooklyn, NY 11201 USA
6.Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
Recommended Citation
GB/T 7714
Guo, Zehua,Xu, Yang,Liu, Ya-Feng,et al. AggreFlow: Achieving Power Efficiency, Load Balancing, and Quality of Service in Data Center Networks[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2021,29(1):17-33.
APA Guo, Zehua.,Xu, Yang.,Liu, Ya-Feng.,Liu, Sen.,Chao, H. Jonathan.,...&Xia, Yuanqing.(2021).AggreFlow: Achieving Power Efficiency, Load Balancing, and Quality of Service in Data Center Networks.IEEE-ACM TRANSACTIONS ON NETWORKING,29(1),17-33.
MLA Guo, Zehua,et al."AggreFlow: Achieving Power Efficiency, Load Balancing, and Quality of Service in Data Center Networks".IEEE-ACM TRANSACTIONS ON NETWORKING 29.1(2021):17-33.
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