A Hybrid Virtual Network Function Placement Strategy for Maximizing the Profit of Network Service Deployment Over Time-Varying Workloads

The emergence of network function virtualization (NFV) has revolutionized the infrastructure and service management of network architecture. It allows network operators to reduce costs and improve the agility of network service deployment. But finding the best VNF placement is a well-known NP-complete problem. Hence, many previous studies either formulate the problem as an Integer Linear Programming (ILP) Problem or propose greedy algorithms. However, solving ILP is time-consuming, while greedy algorithms could be far from optimal solutions. As a result, neither ILP nor greedy can make quick and accurate placement decisions for dynamic traffic workloads. Therefore, we propose a hybrid method that uses less time to maximize the overall profit of network service deployment. Our evaluations based on real backbone network traffic and topology show that our hybrid approach can achieve up to 36% profit improvement compared to a pure greedy approach, while achieving x30 times computation time speedup over a pure ILP approach.