학술논문

Network Function Virtualization-Aware Orchestrator for Service Function Chaining Placement in the Cloud
Document Type
Periodical
Source
IEEE Journal on Selected Areas in Communications IEEE J. Select. Areas Commun. Selected Areas in Communications, IEEE Journal on. 37(3):643-655 Mar, 2019
Subject
Communication, Networking and Broadcast Technologies
Delays
Quality of service
Cloud computing
Virtualization
Optimization
Computer architecture
Software
Network function virtualization
network softwarization
cloud computing
mobile computing
service function chain
high availability
next generation network
next generation mobile networks
5G mobile communication
Language
ISSN
0733-8716
1558-0008
Abstract
Network function virtualization (NFV) has been introduced by network service providers to overcome various challenges that hinder them from satisfying the growing demand for networking services with higher return-on-investment. The association of NFV with the leading technologies of information technology virtualization and software defined networking is paving the way for flexible and dynamic orchestration of the VNFs, but still, various challenges need to be addressed. The VNFs instantiation and placement problems on data center’s (DC) servers are key enablers to achieve the desired flexible and dynamic NFV applications. In this paper, we have addressed the VNF placement problem by providing a novel mixed integer linear programming (MILP) optimization model and a novel heuristic solution, Betweenness centrality Algorithm for Component Orchestration of NFV platform (BACON), for small- and large-scale DC networks. The proposed solution addresses the VNF placement while taking into consideration the carrier-grade nature of the NFV applications and at the same time, minimizing the intra- and end-to-end delays of the service function chain (SFC). Also, the proposed approach enhances the reliability and the quality of service (QoS) of the SFC by maximizing the count of the functional group members. To evaluate the performance of the proposed solution, this paper conducts a comparative analysis with an NFV-agnostic algorithm and a greedy-k-NFV approach, which is proposed in the literature work. Also, this paper defines the complexity and the order of magnitude of the MILP model and BACON. BACON outperforms the greedy algorithms especially the greedy-k-NFV solution and has a lower complexity, which is calculated as $O((n^{3}-n^{2})/2)$ . The simulation results show that finding an optimized VNF placement can achieve minimal SFCs delays and enhance the QoS accordingly.