Research Article
On Rule Placement for Multi-path Routing in Software-Defined Networks
@INPROCEEDINGS{10.1007/978-3-319-28910-6_6, author={Jie Zhang and Deze Zeng and Lin Gu and Hong Yao and Yuanyuan Fan}, title={On Rule Placement for Multi-path Routing in Software-Defined Networks}, proceedings={Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings}, proceedings_a={COLLABORATECOM}, year={2016}, month={2}, keywords={Software defined network Multi-path routing Rule placement Optimization}, doi={10.1007/978-3-319-28910-6_6} }- Jie Zhang
Deze Zeng
Lin Gu
Hong Yao
Yuanyuan Fan
Year: 2016
On Rule Placement for Multi-path Routing in Software-Defined Networks
COLLABORATECOM
Springer
DOI: 10.1007/978-3-319-28910-6_6
Abstract
Software Defined Network (SDN) is a newly emerging network architecture with the core concept of separating the control plane and the data plane. A centralized controller is introduced to manage and configure network equipments to realize flexible control of network traffic. SDN technology provides a good platform for application-oriented network innovations to improve network resource utilization, simplify network management, and reduce operating cost. With SDN devices (e.g., OpenFlow switches), routing becomes more flexible by simply changing the contents of flow tables. The flow table is usually implemented in expensive and power-hungry Ternary Content Addressable Memory (TCAM), which is thus capacity-limited. How to optimize the network performance with the consideration of limited TCAM capacity is therefore significant. For example, multi-path routing (MPR) has been widely regarded as a promising method to promote the network performance. However, MPR is at the expense of additional forwarding rule, imposing a heavy burden on the limited flow table. In this paper, we are motivated to investigate an MPR schedule problem with joint consideration of forwarding rule placement. An integer linear programming (ILP) model is formulated to describe this optimization problem. To address the computation complexity, we further design a three-phase heuristic algorithm. Its high efficiency is validated by the fact that it much approaches the optimal solution, according to our extensive simulation studies.