Optimizing Service Function Chaining Migration With Explicit Dynamic Path

Abstract

Network Function Virtualization (NFV) can support customized on- demand network services with flexibility and cost-efficiency. Virtual Network Function (VNF) instances need to be scaled out, scaled in, and reallocated across the NFV infrastructure (NFVI) to avoid a violation of service agreements when the demand traffic changes. However, selecting the new placement of VNFs for migrating a service function chain (SFC) is an issue of efficient NFV control. We propose two novel integer linear programming (ILP) models and two approximation algorithms for SFC placement and migration to maximize the cost-efficiency of an NFV network regarding the changes of service demands and dynamic routing. The ILP models allow us to obtain the optimal solutions of SFC placement and migration with explicit dynamic paths. The approximation migration results provided by our proposed heuristic and reinforcement learning algorithms are close to the optimal solution. Evaluation results carried out with real datasets and synthetic network topologies provide a helpful suggestion of a migration strategy for an NFV service provider to optimize the operating cost of an NFV network in the long term.

Keywords

• NFV
• SFC
• service migration
• optimization
• machine learning