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Heuristic Cloudlet Allocation Policy for QoS Improvement in Cloud

Cloud computing is an ocean of configurable set of computing resources allowing pervasive admittance to these resources located in the data centers. Quality of service (QoS) is a crucial research direction in cloud that identifies various critical issues such as optimizing response time, degree of load imbalance, makespan improvement, completion time of cloudlet (Task), loss of bandwidth, and the latency of the entire system. Scheduling plays a major role for connectivity of task (cloudlet) to virtual machine. It is tedious to design a distribution policy that works well for the binding of cloudlets to relevant resources in multi-faceted environments without deriding the QoS indicator. This work focuses a heuristic for distribution planning of cloudlets that appositely works in multi-faceted environments, in which selection of cloudlet performed in a hybrid manner such as shortest job first (SJF) follows by longest job first (LJF) manner. Finally, the cloudlets ceded to maximum power factor virtual machines (VMs) such as when this machine overloaded, load is transfer to next VM which has maximum power factor. This strategy uniformly distributes the cloudlets among the VMs that balance the load in excellent way. This also minimizes the overall finalization time (makespan) on the VMs and also on the hosts in the data center, reduces processing cost and maximizes the resource utilization. The experiment is carried out using CloudSim 3.0.3 toolkit, expanding some of the base classes of the simulator and comparing the results with existing distribution policy which shows our proposed policy outperformed the existing policy.