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GAMap: A Genetic Algorithm based Effective Virtual Data Center Re-Embedding Strategy

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
  • Anurag Satpathy
  • Manmath Narayan Sahoo
  • Chittaranjan Swain
  • Muhammad Bilal
  • Sambit Bakshi
  • Houbing Song
<mark>Journal publication date</mark>21/12/2023
<mark>Journal</mark>IEEE Transactions on Green Communications and Networking
Publication StatusE-pub ahead of print
Early online date21/12/23
<mark>Original language</mark>English


Network virtualization allows the service providers (SPs) to divide the substrate resources into isolated entities called virtual data centers (VDCs). Typically, a VDC comprises multiple cooperative virtual machines (VMs) and virtual links (VLs) capturing their communication relationships. The SPs often re-embed VDCs entirely or partially to meet dynamic resource demands, balance the load, and perform routine maintenance activities. This paper proposes a genetic algorithm (GA)-based effective VDC re-embedding (GAMap) framework that focuses on a use case where the SPs relocate the VDCs to meet their excess resource demands, introducing the following challenges. Firstly, it encompasses the re-embedding of VMs. Secondly, VL re-embedding follows the re-embedding of the VMs, which adds to the complexity. Thirdly, VM and VL re-embedding are computationally intractable problems and are proven to be NP-Hard. Given these challenges, we adopt the GA-based solution that generates an efficient re-embedding plan with minimum costs. Experimental evaluations confirm that the proposed scheme shows promising performance by achieving an 11.94% reduction in the re-embedding cost compared to the baselines.