期刊
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
卷 18, 期 3, 页码 2929-2942出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2021.3066847
关键词
Resilience; Load modeling; Control systems; Switches; Process control; Optimization; Load management; Hybrid software defined networks; multi-period controller placement; multi-objective Pareto Frontier
资金
- Science and Engineering Research Board, Department of Science and Technology, Government of India [SRG/2020/001623]
Although SDN is gaining popularity in network operations, there is a lack of research on the placement of SDN controllers in hybrid SDN networks. This paper introduces and formulates a multi-objective control plane dimensioning problem, taking into account network resilience, flow-setup latency, and controller load balancing as objectives.
Software defined networking (SDN) is gaining the confidence of network operators, who are increasingly motivated to introduce it in their networks. However, since SDN is based on centralized control plane (decoupled from the data plane), it is incompatible with distributed control plane of legacy networks. As adopting SDN by complete overhaul of existing legacy networks is infeasible, several solutions propose to incrementally adopt SDN in legacy networks, resulting in hybrid SDN/legacy networks during the transition phase. While strategies to effectively introduce SDN switches in legacy networks have received significant attention in the literature, the same is not true for the associated SDN controller placement in hybrid SDN networks. In this paper, we introduce and formulate the multi-objective control plane dimensioning problem in hybrid SDN/legacy networks, considering network resilience, flow-setup latency and controller load balancing as objectives. We propose optimization models for both the single-objective as well as the multi-objective controller placements. We model both controller processing latency (based on queueing theory) and inter-controller network state synchronization latency (based on Steiner trees) without compromising linearity of the optimization formulations. A genetic algorithm-based heuristic is presented to efficiently deduce the approximate Pareto frontier for our problem. Extensive simulations over a large number of real networks establish the effectiveness of our approach in terms of several single-objective and multi-objective performance metrics.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据