Task Scheduling Using Meta-Heuristic Optimization Techniques in Cloud Environment

Abstract

Cloud computing provides various services for the resource allocation like computation, storage in a virtualization. The virtual machine in cloud, allocate the job and schedules the task efficiently. The task scheduling and resources utilization are the key issues in the cloud environment. Scheduling allocate different types of job in the resources. Scheduling is decided based on the feedback of the Quality of Services (Qos). Quality of Service is the guaranteed service which handles the different task in the job allocation. Therefore, in order to schedule the intensive data, numerous heuristic algorithms have been recommended such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Ant Colony Optimization Algorithms (ACO), Artificial Bee Colony Algorithms (ABC), to solve the task scheduling and resource matter.  This project proposes a Bat Algorithm to solve task scheduling and resource allocation problem in cloud computing. The Bat algorithm is implemented to enforce a better efficient scheduling mechanism which will increase the performance and efficiency of the system by minimizing the execution time (makespan), execution cost, deadline and load balancing. This service is used to complete the task as soon as possible without any delay in task scheduling.