학술논문

GAMap: A Genetic Algorithm-Based Effective Virtual Data Center Re-Embedding Strategy
Document Type
Periodical
Source
IEEE Transactions on Green Communications and Networking IEEE Trans. on Green Commun. Netw. Green Communications and Networking, IEEE Transactions on. 8(2):791-801 Jun, 2024
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Costs
Substrates
Genetic algorithms
Statistics
Sociology
Servers
Dynamic scheduling
Virtual data centers
resource management
data centers
genetic algorithm
re-embedding
Language
ISSN
2473-2400
Abstract
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 $\mathcal {NP}$ -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.