AN IMPROVED TASK SCHEDULING FRAMEWORK IN CLOUD COMPUTING USING ADAPTED GENETIC LOAD BALANCING MUTATED BINARY PARTICLE SWARM OPTIMIZATION (International Journal of Food and Nutritional Sciences, 2022)
In cloud computing environment tasks are allocated among virtual machines (VMs) having different length, starting time and execution time. Therefore, balancing these loads among VM is a key factor. Load balancing has to be carried out in such a manner that all VMs should have balanced to achieve optimal utilization of its capabilities and improve the system performance. In this work, proposed a load balancing and task scheduling technique by using Load Balancing Mutated Binary Particle Swarm Optimization (LBMBPSO) with multi-objective concept to schedule tasks over the available cloud resources that minimizes the makespan and maximizes resource utilization. This is achieved by having proper information among the tasks and resources within the datacentre. This work adopts concepts of the DNA representation and the mutation operator of genetic algorithms. The proposed LBMBPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. The proposed scheduling algorithm is implemented by using CloudSim simulator. Simulation results clearly shows that proposed scheduling algorithm performs better in reducing make span and increases the resource utilization than other existing techniques.