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%. KEYWORDSnetwork function placement, network function virtualization, nonlinear programming, service chains Int J Commun Syst. 2020;33:e4222.wileyonlinelibrary.com/journal/dac

Large-scale Wi-Fi networks have encountered several critical issues of access point (AP) management such as manual configuration, channel interference, and unbalanced loads, which should be carefully addressed to ensure efficient system performance. Since most of the commercial Wi-Fi products are proprietary and hardware-dependent, some recent studies have aimed at introducing open and programmable solutions. Unfortunately, the studies demand additional protocols and software agents but cannot provide complete solutions. To this end, this experience paper presents the design, prototype implementation, and evaluation of SAMF, which is an open, programmable, and generic framework for access point management in large-scale Wi-Fi networks. By adopting the concept of SDN technology and OpenFlow protocol, SAMF can readily be deployed on low-cost commodity access point hardware and a cloud-based controller, while enabling new network services to be integrated rapidly. Furthermore, experimental results confirm that the framework can significantly reduce operational costs since it accelerates the AP configuration process by approximately 15 times. Besides, SAMF can increase system throughput up to 26.5% and improve the balanced degree of the system by about 40%.

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