Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

In the current scenario, Cloud computing is providing services to IoT-sensor based applications in task offloading. In time-sensitive real-time applications, latency is a major problem in cloud computing. Due to exponential growth in IoT-sensor applications huge amount of multimedia data is produced and only the use of cloud computing decreases the efficiency of quality of service (QoS) in IoT-sensor applications. Fog computing uses to resolve the aforementioned issues in cloud computing. Fog computing accomplishes the low-latency requirement of QoS in time-sensitive real-time IoT-sensor applications. Thus the tasks of IoT-sensor applications are computed by various fog nodes. In this paper, a meta-heuristic scheduler Smart Ant Colony Optimization (SACO) task offloading algorithm inspired by nature is proposed to offload the IoT-sensor applications tasks in a fog environment. The proposed algorithm results are compared with Round Robin (RR), throttled scheduler algorithm and two bio-inspired algorithms such as modified particle swarm optimization (MPSO) and Bee life algorithm (BLA). Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA. Proposed technique reduces the task offloading time by 12.88, 6.98, 5.91 and 3.53% in comparison to Round Robin (RR), throttled, MPSO, and BLA.