Energy-Efficient Resource Management of Virtual Machine in Cloud Infrastructure

Abstract

AbstractLarge organizations and business centers use cloud services as a computing technology for their business purposes. Nevertheless, the use of cloud computing has resulted in creation of huge data centers. The major issues that occur in data centers are managing the infrastructural resources, maintaining the cost of applications (tasks), security, and high usage of energy. It represents cloud computing provides resources based on the principle of virtualization and pay-as-you-go model. The resources such as storage, CPU, network, and memory that are available in virtual machine need to be monitored frequently. This resources management has become a wide area of research. The optimization algorithm called Genetically Enhanced Shuffling Frog Leaping Algorithm (GESFLA) is implemented for the VM allocation and execution of tasks. The idea behind the proposed work is to address some of the issues such as minimizing the power consumption, costs of the running application, and to optimize the resource usage. Cloudsim toolkit is used to find the efficiency of this proposed algorithm with a Genetic Algorithm and Particle Swarm Optimization (GAPSO). Experiments are conducted using PlanetLab workload and Google Cluster Datasets which is very huge data. The experimental results indicate GESFLA’s superiority over GAPSO in terms of resource usage ratio, time to migrate the VMs, and total energy consumption.The proposed algorithm increases the performance of data center by maximizing resource utilization by 16% and migration time by 17%. Also, energy consumption is reduced in comparison with the existing algorithm GAPSO by 6%.KeywordsGenetic algorithmShuffling frog leaping algorithmParticle swarm optimizationResource usageVirtual machine migration