A community detection based approach for Service Function Chain online placement in data center network

Abstract

With the emerging paradigm of Network Function Virtualization (NFV), the Internet Service Provider (ISP) can outsource their service functions to the cloud data center (DC) to reduce Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). In this paper, we study the virtual network function (VNF) online placement and migration problem in DC considering user’s Service Function Chain (SFC). In order to solve the practical problems, we take the data center topology, Basic Resource Consumption, multi-tenancy, flow characteristics, and VNF relationship into consideration. Firstly, we formulate this problem into a dynamic programming model with the aim to minimize the average operational cost in a long term. To reduce the complexity of the online decision, an online two-stage heuristic (OTSH) algorithm is designed to optimally place SFCs. The OTSH consists of a community detection based differentiated greedy algorithm for SFC mapping and an offline iterative migration algorithm for VNF migrating. At last, the joint online heuristic algorithm is proven to make intelligent predictions based on the historical traffic and provide good performance guarantees by simulation.