Joint Optimization of Virtual Function Migration and Rule Update in Software Defined NFV Networks
Jie Zhang, Deze Zeng, Lin Gu, Hong Liang Yao, Muzhou Xiong · 2017
Emerging technologies such as Software-Defined Networks (SDN) and Network Function Virtualization (NFV) promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery. To catch up with the time- varying traffic demands, the network changes frequently. We should come up with a sequence of instructions to manipulate the starting network into the goal network, while preserving the network semantics correctness (e.g., freedom of loops, bandwidth guaranteeing). In this case, how to migrate the virtual network functions (VNF) and update the flow forwarding rules efficiently is an important and challenging problem. In this paper, we are motivated to address the migration of VNF and flow update rule problem with joint consideration of migration cost and update delay. The problem is first formulated into a mixed integer non-linear programming (MINLP). By linearizing and relaxing the MINLP, we then present a polynomial-time two-stage heuristic algorithm. The high efficiency of our algorithm is extensively validated by simulation based studies by the fact that it performs much closer to the optimal solution.