Cost minimization in placing service chains for virtualized network functions

作     者：Wang, Chien Ting Lin, Ying-Dar Wang, Chih-Chiang Lai, Yuan-Cheng 

作者机构：Natl Chiao Tung Univ Grad Degree Program Network & Informat Syst Hsinchu Taiwan Acad Sinica Hsinchu Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Kaohsiung Univ Appl Sci Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan 

出 版 物：《INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS》 (国际通信系统杂志)

年 卷 期：2020年第33卷第4期

页      面：e4222-e4222页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Chiao Tung University 

主　　题：network function placement network function virtualization nonlinear programming service chains 

摘      要：Network function virtualization (NFV) places network functions onto the virtual machines (VMs) of physical machines (PMs) located in data centers. In practice, a data flow may pass through multiple network functions, which collectively form a service chain across multiple VMs residing on the same or different PMs. Given a set of service chains, network operators have two options for placing them: (a) minimizing the number of VMs and PMs so as to reduce the server rental cost or (b) placing VMs running network functions belonging to the same service chain on the same or nearby PMs so as to reduce the network delay. In determining the optimal service chain placement, operators face the problem of minimizing the server cost while still satisfying the end-to-end delay constraint. The present study proposes an optimization model to solve this problem using a nonlinear programming (NLP) approach. The proposed model is used to explore various operational problems in the service chain placement field. The results suggest that the optimal cost ratio for PMs with high, hybrid, and low capacity, respectively, is equal to 4:2:1. Meanwhile, the maximum operating utilization rate should be limited to 55% in order to minimize the rental cost. Regarding quality of service (QoS) relaxation, the server cost reduces by 20%, 30%, and 32% as the end-to-end delay constraint is relaxed from 40 to 60, 80, and 100 ms, respectively. For the server location, the cost decreases by 25% when the high-capacity PMs are decentralized rather than centralized. Finally, the cost reduces by 40% as the repetition rate in the service chain increases from 0 to 2. A heuristic algorithm, designated as common sub chain placement first (CPF), is proposed to solve the service chain placement problem for large-scale problems (eg, 256 PMs). It is shown that the proposed algorithm reduces the solution time by up to 86% compared with the NLP optimization model, with an accuracy reduction of just 8%.