An Enhanced Load Balancing Strategy in Cloud with Energy Constraints

47 Abstract— In this paper we enhance the existing Cloud Load balancing Strategy and provide an efficient energy saving concept for the Cloud Servers by considering the Energy factor. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: distribute the workload evenly to the smallest set of servers operating at a n optimal energy level, while observing QoS constraints, such as the response time. Our model applies to clustered systems; the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling.