Telecommunications Science ›› 2022, Vol. 38 ›› Issue (11): 57-72.doi: 10.11959/j.issn.1000-0801.2022275

• Research and Development • Previous Articles     Next Articles

Improved grey wolf optimization algorithm based service function chain mapping algorithm

Yue ZHANG, Junnan ZHANG, Xiaochun WU, Chen HONG, Jingjing ZHOU   

  1. School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou 310018, China
  • Revised:2022-10-20 Online:2022-11-20 Published:2022-11-01
  • Supported by:
    The Natural Science Foundation of Zhejiang Province(LY19F020002);The Natural Science Foundation of Zhejiang Province(LY19F020006);Zhejiang Key Laboratory of New Network Standards and Application Technology(2013E10012)


With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.

Key words: network function virtualization, service function chain, resource optimization

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