SCM: A method to improve network service layout efficiency with network evolution

PLOS ONE, Nov 2019

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.

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SCM: A method to improve network service layout efficiency with network evolution

December SCM: A method to improve network service layout efficiency with network evolution Qi Zhao 0 1 Chuanhao Zhang 1 Zheng Zhao 1 0 Computer Science and Technology College, Jilin University , Changchun, Jilin , China , 2 Department of Public Security Technology, Railway Police College , Zhengzhou, Henan , China , 3 National Digital Switching System Engineering & Technological R&D Center , Zhengzhou, Henan , China , 4 Department of Network Engineering, Zhengzhou Science and Technology Institute , Zhengzhou, Henan , China 1 Editor: Xiangxiang Zeng, Xiamen University , CHINA Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of ªsoftware defined network + network function virtualizationº (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. - Data Availability Statement: All data are available within the paper. Funding: This work is supported by the Ministry of Public Security Technical Research Plan under Grant No.2016JSYJB38 (to CZ) and the Scientific and Technological Research Program under Grant No.172102210441 (to CZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Introduction Middleboxes [1], hardware-based network services, are widely deployed in the Internet and being recognized as important components of networks, such as their uses as firewalls, intrusion detection systems/intrusion prevention systems (IDS/IPS), load balancers, agencies, network address translators (NAT), and wide area network (WAN) optimizers etc. Service chains [2] are created by combining these service instances according to network policies and user requirements. Service chains satisfy various needs of users and provide value-added services to networks. The flexibility and expansibility of the service deployment can be improved by network function virtualization (NFV) [3±5]. In NFV, special hardware middleboxes are replaced by virtual services, thus service chain customization and dynamical creation can be achieved. In addition, software defined networks (SDN) [6±8] are widely used to orchestrate network services according to policies and control traffic to pass special service chains [9±12]. The "SDN +NFV" network paradigm [13] provides a flexible, scalable, and adaptable architecture for deploying virtual services and creates new opportunities for service chain management. Based on the "SDN+NFV" architecture, there has been extensive researches about service chain deployment [12, 14±17]. In these studies, service chains are deployed with consideration of the quality of service, resource allocation, and network security. However, these service chain placement approaches only consider the current network static status, and the changes of the network state are neglected. With network evolution, some old flows may disappear and new flows may arise. Therefore, the status of network changes and the service chain deployment may lose optimality with network evolution, resulting in a waste of network resources and energy. Moreover, the quality of services will decrease due to the degraded service chain layout. This problem is difficult to resolve in service chain deployment due to the limited ability to anticipate the evolution of network states. However, if the deployment of service chains is dynamic throughout their life cycles, the network resource allocation can be adjusted dynamically to adapt to the changing network status. In this paper, a service chain migration (SCM) framework is proposed to address this problem, in which service chains are migrated dynamically to adapt to network evolution. SCM satisfies policy demands and improves the effectiveness of the network service instance layout. We model SCM as an integer linear programming (ILP) problem, and particle swarm optimization (PSO) [18] is used to solve the SCM problem. The contributions of this work are as follows. 1. Two scenarios are shown to illustrate that the efficiency of the network resource layout decreases with network evolution and that service chain migration is utilized to improve the situation. 2. An SCM framework is proposed to optimize the network resource layout. We (...truncated)


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Qi Zhao, Chuanhao Zhang, Zheng Zhao. SCM: A method to improve network service layout efficiency with network evolution, PLOS ONE, 2017, Volume 12, Issue 12, DOI: 10.1371/journal.pone.0189336