Improved Genetic Algorithm for Monitoring of Virtual Machines in Cloud Environment

Resource utilization and energy need to be carefully handled for achieving virtualization in the cloud environment. An important aspect to be considered is that of load balancing, where the workload is distributed so that a particular node does not become overburdened with tasks. Improper load balancing will lead to losses in terms of both memory as well as energy consumption. The resources should be scheduled in a cloud in such a way that users obtain access at any time and with possibly less energy wastage. The proposed algorithm uses an improved Genetic Algorithm that helps reduce overall power consumption as well as performs scheduling of virtual machines so that the nodes are not loaded below or above their capacity. In this case, each chromosome in the population is considered to be a node. Each virtual machine is allocated to a node. The virtual machines on every node correspond to the genes of a chromosome. Crossover and mutation operations have been performed after which optimization techniques have been used to obtain the resulting allocation of tasks. The proposed approach has proved to be competent with respect to earlier approaches in terms of load balancing and resource utilization.