IMPROVED RESOURCE ALLOCATION ALGORITHMS FOR CLOUD

Past few years, there have been several attempts to reduce energy consumption of data centers. High Power Consumption for Virtual Machines (VMs) is the main issue in Cloud Computing. In this paper, the Virtual Machine (VM) consolidation problem used to reduce energy consumption of data centers while satisfying Quality of Service (QoS) requirements is addressed. The distributed system architecture to perform dynamic VM consolidation to improve resource utilizations of Physical Machines (PMs) and to reduce their energy consumption has been presented. The Proposed algorithm is the hybrid algorithm, which is the combination of Improved Best Fit Resource Allocation (IBFRA) and Minimum Migration (MM). In proposed approach, the user tasks or workloads are initially allocated to VMs based on the Improved Best Fit Resource Allocation (IBFRA) algorithm. Minimum Migration is used for migration process, to reduce the energy by avoiding the use of unused system and efficient usage of unused memory. This method monitors all the virtual machines in all cloud locations centre. It identifies the state virtual machines whether it is sleep, idle, or running (less or over) state. This combination of algorithm will save more energy in cloud and improve the efficiency and quality of the cloud services. Experimental results indicate that the proposed platform yields to the optimal solution for a limited time-frame. The performance of proposed estimation module and state of the art IBFRA-MM estimator is compared and assessed. The comparative results prove that the proposed module attains encouraging gain over its peers.