Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Abstract

Minimizing the energy consumption and resource usage in cloud computing environment is one of the key research issues. Energy aware resource allocation is used to optimize the power consuming by computer resources and storage in cloud. The proposed system is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Using migration for minimizing the number of active physical nodes the dynamic single threshold VM consolidation leverages fine-grained fluctuations in the workloads and continuously reallocates VMs . A genetic algorithm based power-aware scheduling of resource allocation (G-PARS) has been proposed to solve the dynamic virtual machine allocation policy problem. The experiment results show that strategy that has been proposed has a better performance than other strategies, not only in high Quality Of Service(QoS) but also in less energy consumption.