An enhanced energy efficient model for workloads and virtual machine management in cloud

Abstract

Cloud technology provides a pool of shared virtual resources with the advent of internet and the use newlineof cloud is growing because of its features. As the demand on storing the data on cloud has been newlineincreasing, the consumption of energy in data centers also has become an vital issue. Therefore, this newlinethesis emphasizes on managing the energy using software techniques in a cloud environment. newlineMethodologies using energy efficiency like Dynamic Voltage Frequency Scaling and virtual newlinemachine consolidation operates efficiently when there is less workload in data centers. Cloud data newlinecenters may experience raw workloads which adversely affects the performance of the system. To newlinehandle the scenario, it s required a novel technique to improve the energy efficiency in cloud. newlineBrownout model or framework in cloud has proven to be a best and suitable approach which newlineimproves energy in cloud by turning off the components or services which are not required to be newlineexecuted all the time. Load balancing is one the method to consolidate the workloads in data newlinecenters. Hence it is managed well by Brownout.