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

Chi-Chen Yang,Satyajit Padhy,Jerry Chou

doi:10.1109/access.2021.3094664

Abstract

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.

Highlights

The emergence of network function virtualization (NFV) has revolutionized the infrastructure and service management of network architecture
Our evaluations show that our hybrid approach can achieve up to 38% profit improvement comparing to a pure greedy approach, while achieving x30 times computation time speedup over a pure Integer Linear Programming (ILP) approach
We know the strength of ILP is that the ILP solver can always optimize the result according any given objective function, while greedy algorithms normally are ad-hoc strategy designed for single objective or intuition

Summary

Introduction

The emergence of network function virtualization (NFV) has revolutionized the infrastructure and service management of network architecture. Through NFV, network operators can reduce their cost on Capital expenditures (CAPEX), operating expenses (OPEX), and power consumption, and improve the time and flexibility of network service deployment. It has been actively studied by industry [1] and research communities [2] in recent years. One of the key challenges of NFV orchestration is the VNF placement problem, which places the VNFs of a set of service function chain requests (SFCRs) on physical nodes in order to satisfy the resource and traffic demand of SFCRs. The goal of the placement problem is to minimize the deployment cost and maximize the service quality of the SFCRs. In general, the deployment cost includes the resource consumption from computing power and network bandwidth, and service quality is measured by the end-to-end latency delay of the user

Objectives

Results

Conclusion