Multicriteria dragonfly graph theory based resource optimized virtual network mapping technique for home medical care service provisioning in cloud

Abstract

The cloud offers more services across multiple infrastructures and rapidly growing areas of development in medical care. In the cloud, Network virtualization allows multiple isolated virtual networks (VNs) for flexible sharing of network resources. The virtual network mapping in Network virtualization provides the dynamic virtual node and link resources to satisfy the user needs. The major challenges of cloud computing are optimally and resourcefully responds to each user service requests with minimum time. To address these problems in distributed and hybrid cloud environments, Multicriteria Dragonfly based Graph Theory Resource Optimized Virtual Network Mapping (MD-GTROVNM) technique is introduced. The main objective of the MD-GTROVNM technique is to improve the efficiency of virtual network request mapping with less resource utilization. In the MD-GTROVNM technique, Multicriteria Dragonfly based Graph Theory performs both virtual node mapping and link mapping with reasonable resource utilization such as CPU, memory, and bandwidth. In node mapping, the Multicriteria Dragonfly optimization technique is applied to find the optimal physical node among the population that satisfies the resource constraints. The proposed Multicriteria Dragonfly optimization algorithm achieved a more optimal solution for virtual network mapping. The experimental scenario is carried out with various parameters such as mapping efficiency, computation time, Request acceptance ratio and memory consumption with a number of VN requests. The observed results confirm that the MD-GTROVNM technique effectively increases the mapping efficiency, Request acceptance ratio and minimizes the computation time as well as memory consumption when compared to the existing techniques.