Energy efficient strategy for task allocation and VM placement in cloud environment

The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission. The cloud computing allows users to utilize and access resources from the data centers when they demand rather than owning it. Load balancing is challenging when the workload is dynamic. A two level scheduling is proposed for load balancing taking into consideration the energy efficiency, power consumption and effective resource utilization satisfying the Quality of Service (QoS) service for the consumers with reduced SLA violation. In the first level of scheduling main focus is on the Task scheduling to VMs in which the technique of genetic algorithm and particle swarm optimization (PSO) is combined, a hybrid approach. The second level includes the placement of Virtual Machines to suitable Physical Machines by using Power aware best-fit algorithm in which the number of active servers are reduced and energy consumption is reduced. The paper focuses on study of load balancing and VM placement techniques.