A Novel Hybrid GAACO Algorithm for Cloud Computing using Energy Aware Load Balance Scheduling

The process of spreading workloads and computing resources through one or more servers is cloud load balancing. This method of delivery ensures maximum response time at low throughput. The workload is divided between two or more servers, storage devices, network connectivity or other data centers, thus allowing efficient use of resources and response time for the device. To ensure device cloud performance, an energy-aware operating model is implemented which is used for load balancing. The method describes an energy-optimal operating regime and seeks to optimize the number of servers that run under this regime. To save resources, idle and lightly charged servers are moved to one of the sleeping states. A Genetic Algorithm and Ant Colony Optimization (GAACO) framework for energy-efficient cloud computing is specified based on meta-heuristics inspired by nature, namely novel QoS based on hybrid genetic ant colony, and load scheduling algorithm. The proposed energy-aware QoS load balance scheduling was implemented in an ns3 simulator and the performance was evaluated in terms of packet loss ratio and throughput.