VNR-GA: Elastic virtual network reconfiguration algorithm based on Genetic metaheuristic

Abstract

Cloud Computing offers elasticity and enhances resource utilisation. This is why its success strongly depends on the efficiency of the physical resource management. This paper deals with dynamic resource reconfiguration to achieve high resource utilisation and to increase Cloud providers income. We propose a new adaptive virtual network resource reconfiguration strategy named VNR-GA to handle dynamic users' needs and to adapt virtual resource allocation according to the applications' requirements. The proposed algorithm VNR-GA is based on Genetic metaheuristic and takes advantage of resources migration techniques to recompute the resource allocation of instantiated virtual networks. In order to optimally adapt the resource allocation according to customers' needs growth, the main idea behind the proposal is to sequentially generate populations of reconfiguration solutions that minimise both the migration and mapping cost and then select the best reconfiguration solution. VNR-GA is validated by extensive simulations and compared to the most prominent related strategy found in literature (i.e., SecondNet). The results obtained show that VNR-GA reduces the rejection rate of i) virtual networks and ii) resource upgrade requests and thus enhances Cloud Provider revenue and customer satisfaction. Moreover, reconfiguration cost is minimised since our proposal reduces both the amount of migrated resources and their new mapping cost.