期刊
IEEE TRANSACTIONS ON SERVICES COMPUTING
卷 15, 期 1, 页码 188-201出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2019.2927339
关键词
Network function virtualization; service function chaining; delay; resiliency; availability; placement; routing
资金
- National Natural Science Foundation of China (NSFC) [61802018]
- Beijing Institute of Technology Research Fund Program for Young Scholars
- NSFC [61772077, 61370192]
- Beijing Natural Science Foundation [4192051]
- EU [607584, 824019]
This paper investigates the service level agreements and virtual network function placement and routing problem in network function virtualization. It proposes methods to solve these problems through quantitative modeling and algorithm design, and evaluates the proposed algorithms through extensive simulations.
Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming (INLP) formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.
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