Load prediction analysis based on virtual machine execution time using optimal sequencing algorithm in cloud federated environment

Virtual machine (VM) prediction and an effective resource management are the attractive areas in the cloud environment. VM prediction is an important task to execute the jobs for delay minimization and unnecessary states avoidance. Cloud computing attracted towards the increase in a number of applications that run on remote servers in parallel manner. Increase in parallelism reduces the CPU utilization adversely. Hence, the proper VM prediction and management are necessary stages in provisioning scheme. Also time required for allocating jobs is more in existing algorithms due to the number of computations involved. Therefore a novel algorithm is required to improve the performance of the job allocation with makespan reduction. In this paper the new algorithm is proposed that includes the VM capacity and execution time for load prediction and performance improvement purpose. Our proposed research work utilizes the VM clustering and optimization algorithms to improve job sequencing performance. The cost computation prior to clustering includes the VM capacity as a major factor. Clustering of VM with high-cost and isolation of low-cost and high-cost clusters reduces the searching time of VM and solve the imbalance state problem in traditional methods. The optimization algorithm with suitable initialization function reduces the time and steps for selection of VM for suitable job. The proposed model outperformance is established by the selected parameters.