An Evolutionary Algorithm for Solving Task Scheduling Problem in Cloud-Fog Computing Environment

Abstract

Recently, IoT (Internet of Things) has grown steadily, which generates a tremendous amount of data and puts pressure on the cloud computing infrastructures. Fog computing architecture is proposed to be the next generation of the cloud computing to meet the requirements of the IoT network. One of the big challenges of fog computing is resource management and operating function, as task scheduling, which guarantees a high-performance and cost-effective service. We propose TCaS - an evolutionary algorithm to deal with Bag-of-Tasks application in cloud-fog computing environment. By addressing the tasks in this distributed system, our proposed approach aimed at achieving the optimal tradeoff between the execution time and operating costs. We verify our proposal by extensive simulation with various size of data set, and the experimental results demonstrate that our scheduling algorithm outperforms 38.6% Bee Life Algorithm (BLA) in time-cost tradeoff, especially, performs much better than BLA in execution time, simultaneously, satisfies user's requirement.