IMPLEMENTATION OF BFS AND LMM ALGORITHM FOR ENERGY AWARE SCHEDULING

Abstract

The cloud implementation has attracted many scientific, consumer and business application towards it due to its utility. Due to its flexibility and utility energy consumption had increased a lot and becomes critical worldwide problem. Many methodologies were developed over last few years to reduce the energy consumption of cloud data centers. The power Consumption for VMs is reduced through VM consolidation. In this project, the VM consolidation is achieved with the objective to reduce the energy consumption of cloud data centers while satisfying QoS requirements. Distributed architecture is proposed to perform dynamic VM consolidation to improve resource utilizations of VMs and to reduce their energy consumption. The Proposed algorithm is the hybrid algorithm that is the combination of Best Fit Scheduling (BFS) and Local Minimum Migration (LMM). In the proposed approach, the user tasks or workloads are initially allocated to VMs based on the Best Fit Scheduling (BFS) algorithm and Local Minimum Migration (LMM) is used for migration process, to reduce the energy by avoiding the use of an unused system and efficient usage of unused memory. This method monitorsthe state of virtual machines in all cloud locations center and it identifies the state virtual machines whether it is sleep, idle, or running (less or over) state. This combination of an algorithm will save energy in the cloud and improve the efficiency and quality of the cloud services.