SQGA: Quantum Genetic Algorithm-based Workflow Scheduling in Fog-Cloud Computing

Fog computing represents an extension of the Cloud infrastructure, which allows the improvement of the performance of IoT applications. The problem of task scheduling represents a challenge in this type of environment, with the aim of how to allocate the tasks to the different nodes of the Fog-Cloud infrastructure, in order to minimize makespan, cost, response time, and energy. In this paper, we propose SQGA— an algorithm to improve the workflow scheduling in Fog-Cloud environment. This algorithm is based on the quantum genetic algorithm QGA and aims to improve the makespan of applications deployed in the Fog-Cloud computing environment. The proposed SQGA scheduling algorithm is compared to the classical genetic algorithm and the First Come First Served algorithm. The experiment results show that the proposed SQGA algorithm is more efficient in makespan, and adapts better to the available resources.