Time and Cost Aware Meta Task Scheduling Algorithm for Multicloud Environment (TCAMTSA)

Abstract

Multi cloud is an environment which comprised of one or more cloud service providers provide a unanimous service to one or more consumers. This cloud can be utilized for private or public cloud and it is mainly devised to solve vendor lock in problems in the cloud environment. Scheduling is a process of assigning resources and allocate to the user. Scheduling is one of the major problems in the multi cloud environment. Meta task scheduling is suitable for independent tasks and it works based on batch scheduling mechanism. Particle Swarm Optimization (PSO) is meta heuristics-based technique used to solve scheduling problem. Each particle in PSO represents the solution for the problem. The additive weighted function is used as fitness function to solve the scheduling problem. Chaotic inertia weight is used to increase the convergence rate of the optimization.  The proposed algorithm uses PSO with chaotic inertia weight optimization mechanism to reduce makespan and cost. This algorithm outperforms the existing simple PSO.