AN ADAPTIVE COMPUTATIONAL MODEL FOR THRESHOLD BASED VM MIGRATION AND JOB SCHEDULING IN CLOUD

Cloud computing is considered as a most promising technique for offering the strong significant resources for computation for huge data by taking the advantage of virtual machine configurations where multiple operating systems are configured where multiple application applications are deployed to perform the several tasks. However, during peak time, huge number of requests are processed through the virtual machines where overloading phase of virtual machine may occur which may delay the task completion process resulting in degraded performance of cloud computing system. In order to deal with these issue, virtual machine migration strategy is introduced where overloaded virtual machines are migrated to perform the task in optimal time duration which can help to finish the task on the pre-assigned time duration and can save the energy consumption. During last decade, significant amount of work has been carried out in this field of virtual migration but achieving desired performance is still challenging. In order to deal with this issue, here we present a novel approach where we considered resource availability related information for VM allocation. In the next phase, threshold-based migration scheme is implemented based on the computing resources. Finally, an experimental study is presented for VM migration using proposed technique and a comparative study is also presented which shows that proposed approach achieves better performance when compared with the state-of-art techniques of VM migration.