Adaptive Distributed Software Defined Networking

被引:10
作者
Chen, Yanyu [1 ,2 ]
Yang, Yuan [2 ]
Zou, Xiaoyue [1 ]
Li, Qi [1 ,3 ]
Jiang, Yong [1 ]
机构
[1] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China
[2] Tsinghua Univ, Dept Comp Sci, Beijing 100084, Peoples R China
[3] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing 100084, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
SDN; Algorithm; Controller selection problem;
D O I
10.1016/j.comcom.2016.11.009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Distributed Software Defined Networking (SDN) federates multiple controllers in a network to solve the problems in single controller networks, e.g., to improve network reliability and reduce the delay between controllers and switches. However, in the current distributed SDN schemes, the mapping between SDN switches and controllers is statically configured, which may result in uneven load distribution among controllers. These schemes cannot fully benefit from the distributed SDN architecture. In order to address this issue, this paper proposes ESDN, an adaptive elastic distributed SDN architecture. The architecture dynamically selects a minimum number of active controllers that switches attached to, and changes the mapping between switches and controllers according to the network load. Specially, a switch can migrate from one controller domain to another so that the mapping is adaptive to the network load. We formalize the controller selection problem as an optimization problem, and prove that the problem is NP-Hard. We solve the problem by using offline and online algorithms, respectively. With the heuristics, controllers in a network are dynamically changed with respect to the network load. The offline algorithm has an approximation ratio of 2 related to the optimal result, and the online algorithms can find similar number of active controllers within a shorter time. We validate the algorithms and evaluate the performance by simulations. In particular, the number of inactive controllers computed by shrinking action of online algorithm averagely achieves around 92% of the optimal values when the whole network load decreases from 65% controller capacity to 25% controller capacity. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:120 / 129
页数:10
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