Workflow Scheduling Using Optimization Algorithm in Fog Computing

In the cloud computing paradigm data, owners have to put up their data in the cloud. Due to the longest distance between devices and cloud; problem of delay, bandwidth, and jitter is there. Fog computing was introduced to the edge of the network to overcome cloud problems. During the transfer of data between the Internet of Things (IoT) devices and fog node, scheduling of resources and tasks is necessary to enrich quality of service (QoS) parameters. Various optimization and scheduling algorithms were implemented in a fog environment. Still, the fog environment is facing the problem of efficiency, latency, cost, computation time, and total execution time. Earlier PSO (particle swarm optimization) techniques or ACO (ant colony optimization) are provided the solution to NP-hard problems. Over such types of optimization techniques, various optimization algorithms are provided like Dolphin Partner optimization, Grey wolf, Moth-Flame, Firefly, crow, etc. Priority queue, round robin scheduling algorithm implemented on another side for a solution to the problem. In this paper, the implementation comparison of PSO, ACO on the cloud, and Fog is contrasting using iFogSim toolkit. The results of QoS parameters makespan and cost in fog computing are showing enhancement in QoS over cloud computing.