Traffic-Aware and Energy-Efficient vNF Placement for Service Chaining: Joint Sampling and Matching Approach

TLDR

Network function virtualization offers elastic services but must adapt to diverse appliances while cutting capital and operational costs, and service chain deployment must balance latency and cost objectives that are traditionally treated separately. This work investigates energy‑ and traffic‑aware virtual network function placement to jointly minimize operational and network traffic costs. The authors formulate the joint operational and network traffic cost (OPNET) problem, solve it first with a sampling‑based Markov approximation, and then improve convergence by integrating matching theory into a novel SAMA algorithm. Simulations demonstrate that SAMA reduces total incurred cost by up to 19 % compared with existing non‑coordinated placement methods.

Abstract

Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges in terms of adaptation to diverse network appliances and reduction of the capital and operational expenses of the service providers. In particular, to deploy service chains, providers must consider different objectives, such as minimizing the network latency or the operational cost, which are coupled objectives that have traditionally been addressed separately. In this paper, the problem of virtual network function (vNF) placement for service chains is studied for the purpose of energy and traffic-aware cost minimization. This problem is formulated as an optimization problem named the joint operational and network traffic cost (OPNET) problem. First, a sampling-based Markov approximation (MA) approach is proposed to solve the combinatorial NP-hard problem, OPNET. Even though the MA approach can yield a near-optimal solution, it requires a long convergence time that can hinder its practical deployment. To overcome this issue, a novel approach that combines the MA with matching theory, named as SAMA, is proposed to find an efficient solution for the original problem OPNET. Simulation results show that the proposed framework can reduce the total incurred cost by up to 19 percent compared to the existing non-coordinated approach.