期刊
COMPUTER NETWORKS
卷 133, 期 -, 页码 166-182出版社
ELSEVIER
DOI: 10.1016/j.comnet.2018.01.020
关键词
Network Function Virtualization; Cloud computing; Service function chain; Hidden Markov Model
类别
资金
- National Basic Research Program (973 Program) [2013CB329103]
- NSFC Fund [61271165, 61301153, 61401070, 61571098, 61671130]
- Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT)
- 111 Project [B14039]
- Science and Technology Program of Sichuan Province [2016GZ0138]
As Network Function Virtualization (NFV) becomes reality and cloud computing offers a scalable pay-as-you-go charging model, more network operators would like to outsource their Service Function Chains (SFC) to the public clouds in order to reduce the operational cost. Unfortunately, challenges of Quality of Service guarantee still exist while minimizing the operational cost with outsourcing SFC to public clouds. In this paper, we investigate this problem when there are a large number of candidate cloud providers with the consideration of diverse pricing schemes of network functions, additional latency caused by public network, and the relationship between the Virtual Network Function (VNF) performance and its cost. Compared with our previous conference version, we design D-MOSC, an improved deviation based heuristic algorithm to assign the Outsourcing of SFC across multiple clouds based on Hidden Markov Model (HMM). The extensive simulations show that MOSC saves up to 79.2% cost compared with that of deploying network functions in the local network. MOSC also achieves up to 50.7% cost savings compared with the result of the first-fit based optimization algorithm. Compared with the greedy version, D-MOSC achieves up to 26.7% cost savings with the guarantee of latency requirements. (C) 2018 Elsevier B.V. All rights reserved.
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