Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimization

Clients can access various on-demand services and resources through the cloud-fog computing environment. Due to interdependence between activities, business processes are controlled utilizing workflow technology via the cloud, which poses one of the difficulties in optimum use of the resources, which can highly improve the quality of service (QoS) for a better user experience. In addition, it is not easy to schedule workflow applications in a Fog-Cloud environment to find the best balance between makespan, energy consumption and cost. A hybrid GA-modified PSO method is proposed in this research to assign tasks to the resources efficiently. By balancing the burden of dependent activities, the Hybrid GA (Genetic Algorithm)-modified PSO approach attempts to be less makespan, less cost, and minimize the energy consumption across heterogeneous resources in cloud-fog computing settings. The experiment’s findings demonstrate that, in contrast to other algorithms, the Hybrid GA-modified PSO method reduces the overall execution time of the workflow tasks. Moreover, it lowers the cost of execution. The acquired findings further show that, compared to previous algorithms, the proposed approach converges to optimum solutions more quickly and with outstanding quality.