Resource management in fog computing using greedy and semi-greedy spider monkey optimization

With the proliferation in the internet of things (IoT), a variety of delay sensitive applications have emerged over the past few years. Fog computing acts a viable computing paradigm for meeting the requirements of IoT applications. However, heterogeneous IoT tasks, resource constrained fog nodes makes task scheduling a challenging task. Efficient task scheduling reduces the computation cost, latency, energy and enhances the utilization of resources. In this paper, we propose the improved version of the meta-heuristic algorithm spider monkey optimization (SMO). We propose greedy task scheduling SMO (gTS-SMO) and semi-greedy task scheduling SMO (sgTS-SMO) for efficient task scheduling in fog computing environment. The main aim is minimize delay and energy consumption while considering the constraints of deadline and violation time. The system is evaluated based on the parameters of deadline violation time, makespan, energy consumption, total cost, and degree of imbalance. The results shows gTS-SMO reduces the violation time by 13.86%, 88.38% on comparison with sgTS-SMO and particle swarm optimization (PSO) respectively. The results also depicts the outperformance of gTS-SMO in terms of makespan and energy consumption. Makespan is reduced by 6.28%, and 57.75% and energy consumption by 5.74% and 53.65% on comparison with sgTS-SMO and PSO respectively.