Grey Wolf Optimizer for Virtual Network Embedding in SDN-Enabled Cloud Environment

  • Abderrahim BouchairEmail author
  • Sid Ahmed Makhlouf
  • Yagoubi Belabbas
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


Network technologies are dealing with a massive urge to breakthrough the fundamental endorsements of networks. Software-Defined Networking (SDN) is taking the lead in cloud Data Centers (DCs) to ensure the resource management of many policy adaptations, regarding the performance of Network Virtualization (NV) that must find the appropriate hardware components to map either a Virtual Machine (VM) or a virtual link, which resume the general concept of Virtual Network Embedding (VNE). In this paper, a Grey Wolf Optimizer (GWO) is represented as an intelligent approach for solving the VNE problem in the cloud with SDN consolidation. It is a recent meta-heuristic with low complex processing. Our implementation is based on CloudSimSDN that is an extension from the CloudSim simulation tool. The results indicate that maximizing the utilization of localhost resources maintain a considerable amount of energy consumption and consequently will provide better policy management for physical DCs.


Cloud computing Software-Defined Networking Virtual Network Embedding Swarm Intelligence Grey Wolf Optimizer Resource utilization CloudSimSDN 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abderrahim Bouchair
    • 1
    Email author
  • Sid Ahmed Makhlouf
    • 1
  • Yagoubi Belabbas
    • 1
  1. 1.LIO LaboratoryUniversity of Oran1OranAlgeria

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