Resource availability–aware adaptive provisioning of virtual data center networks

Summary Optimal provisioning of virtual networks with varying topologies on their physical counterpart is a major challenge in cloud data centers. Embedding the virtual network on the shared physical infrastructure poses a multitude of challenges to the infrastructure provider. This paper proposes a hybrid approach which adapts to the availability and fragmentation level of residual resources for near‐optimal mapping of multiple virtual data center networks on a substrate data center network. The proposed technique uses coordinated static and dynamic embedding phases, adaptively, to maximize the density of embedding which increases the revenue of the cloud service provider. Also, it judiciously modifies the existing mappings for efficient utilization of resources. Empirical results show that the proposed hybrid approach can attain ∼10% higher acceptance ratio compared with the existing exact and heuristic techniques in the literature. With the proposed mixed integer programming model for static embedding, the average path length is reduced by 20% compared with the existing techniques. This paper proposes a resource availability and fragmentation aware adaptive algorithm for virtual data center network embedding in cloud data centers. A robust mathematical model is designed to embed the incoming VDCNs densely by modifying the existing embeddings, to maximize the IaaS provider's revenue. The concept of average path length reduction to increase the amount of residual link resources is introduced.