Improved load balancing in IAAS cloud using novel weighted threshold load balancer method

Rapid virtualization growth in cloud environment leads to increased tasks, which entails the utilization of virtual machines (VMs). The main research challenge associated with this cloud environment is its reduction of makespan of cloud tasks and balancing the load. Conventionally many algorithms are proposed to resolve these two challenges. However, static load balancing offers poor interest in transferring the load from one node to another node. Also, it operates based on prior data and not on present data. Hence, it mainly lacks in finding the resource and task information and provides improper task assignments to VMs. To resolve such issues, the proposed system uses weighted task based threshold load balancing (WTLB) and proportional-integralderivative (PID) controller. The WTLB technique provides load balancing in Infrastructure as a Service (IaaS) cloud and it eliminates the limitation of transferring the load between nodes. The PID controller gives past and present information to static load balancer i.e. WTLB to improve the operations in IaaS cloud. Then, an efficient strategy is assigned to configure the servers with its incoming task and size for finding its suitable VMs. Hence, the VMS are assigned with best task, which increases the maximum utilization of resources in IaaS model. The proposed method is compared with existing algorithms though various result metrics. The results proves that proposed algorithm balances the load in IaaS cloud than existing algorithm.