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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - GAMap
T2 - A Genetic Algorithm based Effective Virtual Data Center Re-Embedding Strategy
AU - Satpathy, Anurag
AU - Sahoo, Manmath Narayan
AU - Swain, Chittaranjan
AU - Bilal, Muhammad
AU - Bakshi, Sambit
AU - Song, Houbing
PY - 2023/12/21
Y1 - 2023/12/21
N2 - 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.
AB - 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.
KW - Computer Networks and Communications
KW - Renewable Energy, Sustainability and the Environment
U2 - 10.1109/tgcn.2023.3345542
DO - 10.1109/tgcn.2023.3345542
M3 - Journal article
JO - IEEE Transactions on Green Communications and Networking
JF - IEEE Transactions on Green Communications and Networking
SN - 2473-2400
ER -