A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing

Cloud Computing emerged from Grid Computing empowered with the concept of virtualization. The advantage of switching to a virtual environment is quite beneficial, however in cloud computing there are significant problems pertaining to performance and cost optimization because of various kinds of resource requirement and execution of multiple jobs. Thus the level of performance and criteria of SLA to be maintained becomes very arduous due to such constraints. To overcome these constraints and amend the solution quality, an integrated approach of scheduling model and resource cost timeline model labelled as Multi-Faceted Optimization Scheduling Framework (MFOSF) has been endeavouring in this paper in a timely manner. Resource Cost timeline model manifest the relation between user budget and producer cost while scheduling Model based on optimization in performance and cost can be achieved by the help of PSO. Some simulation has been made to evaluate this framework by applying four different metrics a) Cost b) the Makespan c) Deadline d) Resource Utilization. On the basis of aforesaid metrics, experiment outcomes show MFOSF-PSO method is more effective than the other models peculiarly increases 57.4 % in best case scenario.