Cost-Efficient Service Function Chain Orchestration for Low-Latency Applications in NFV Networks

Gang Sun, Gungyang Zhu, Dan Liao, Hongfang Yu, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

With the popularization and development of cloud computing, network function virtualization and service function chain (SFC) provisioning have attracted increasing attention from researchers. Excellent and reliable network service is important for network development. Moreover, as the number of network users increases, network service construction costs become very high. Therefore, an efficient algorithm is necessary to provide an SFC with excellent performance and low resource costs. In this paper, we re-examine the problem of optimizing the deployment of an SFC to provide users with excellent and resource-saving network service. We propose a heuristic, closed-loop feedback (CLF) algorithm to find the shortest path to map an SFC. To solve the problem, we introduce and integrate a restricted Boltzmann machine and cross entropy to improve the performance of CLF. Simulation results demonstrate the excellent performance of CLF. The communication delay is reduced by approximately 20%, the accept ratio improves by approximately 15%, and the algorithm running time decreases by approximately 20%. In addition, the resource utilization ratio increases by approximately 15%, and the resource fragmentation ratio decreases by approximately 50%.

Original languageEnglish
Article number8565965
Pages (from-to)3877-3888
Number of pages12
JournalIEEE Systems Journal
Volume13
Issue number4
DOIs
StatePublished - Dec 2019

Keywords

  • Machine learning
  • network function virtualization
  • provisioning
  • service function chain

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