Resource Allocation Optimization in the NFV-Enabled MEC Network Based on Game Theory

Abstract

Compared with the conventional mobile edge cloud (MEC) network, the network function virtualization (NFV)-enabled MEC network provides new flexibility on the MEC service deployment. Resource wastage owing to dynamic workloads in traditional MEC networks can be overcome through adaptive resource allocation. In this paper, we investigate the resource allocation problem to minimize the operational cost (e.g., energy consumption, capital expenditure) and the average response time in the NFV-enabled MEC network. We consider the problem from the perspective of MEC service deployment, assignment, and routing among the access points (APs) and MEC servers. We propose an user-network cooperation-based algorithm with low-complexity. In the proposed algorithm, the network announces a path-switching rule (i.e., α-approximate deviation) with proportionally shared operational cost, while the APs selfishly choose their paths with the least cost accordingly. We analyze the selfish behaviors of APs with game theory. We prove existence and convergence of α-approximate equilibriums. Also, we evaluate the efficiency of the equilibriums with the price of stability (POS). Furthermore, an enhanced algorithm based on public service advertising (PSA) is proposed to improve the convergence performance and equilibriums efficiency. Through simulations, we show the superiority of the proposed algorithms over existing algorithms (e.g., BnB-SD and greedy routing) on the accuracy and convergence performance (measured by the overall path switching).