Abstract

Optimization job scheduling of virtual machines in a cloud computing for tasks is considered as NP-hard problem specifically for large task sizes in the cloud. Hence many techniques for job scheduling have been presented previously but they did not consider the combined task scheduling and resource allocation, which reduces the flexibility, increase traffic, congestion, and reduces computation processing time. Hence a novel technique, namely Multi Optimized Job scheduling Framework for VM with enhanced migration in a Multi Cloud Environment has been proposed, in which the load balancers with multi-level optimizations that utilizes the runner root algorithm and Differential evolution algorithm with Levy distribution to schedule the job and determines the VM to be allotted for the job based on international and national level optimization. Moreover, the previous techniques concentrate only on the migration that extends VM lifespan, lacking Quality of Service (QoS) and unsatisfied the end users. Hence a novel technique Active Inactive data migration algorithm is used to prevent fluctuating migration between Virtual Machines and recursive algorithm keeps on iterating the same operation on the server with the lowest virtual load and Optimum Cost Function is to prevent unnecessary migration cost. During VM migration, several applications were affected during a live VM migration that caused a network fault, which is eliminated by a novel Data replacing approach which is used to transfer the exact size of data to the active PM. Overall, the proposed method is to perform an efficient job scheduling in multi cloud environment with optimized VM migration.

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