The most important requirement in cloud computing environment is the task scheduling which plays the key role of efficiency of the whole cloud computing facilities. Task scheduling in cloud computing means that to allocate best suitable resources for the task to be execute with the consideration of different parameters like time, cost, scalability, make span, reliability, availability, throughput, resource utilization and so on. The proposed algorithm considers reliability and availability. Most scheduling algorithms do not consider reliability and availability of the cloud computing environment because the complexity to achieve these parameters. We propose mathematical model using Load Balancing Mutation (balancing) a particle swarm optimization (LBMPSO) based schedule and allocation for cloud computing that takes into account reliability, execution time, transmission time, make span, round trip time, transmission cost and load balancing between tasks and virtual machine .LBMPSO can play a role in achieving reliability of cloud computing environment by considering the resources available and reschedule task that failure to allocate. Our approach LBMPSO compared with standard PSO, random algorithm and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that LBMPSO can save in make span, execution time, round trip time, transmission cost.
Different criteria have been evaluated in REF for the PSObased scheduling algorithm that has developed to increase the efficiency of the load balancing scheduling process in clouds.
Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments
Procedia Computer Science
Computer Science