Cloud Brain Fog Scheduler Machine Learning-Enhanced Task Allocation

Fog computing bridges the advantages of cloud computing with edge computing, enhancing service delivery, reducing latency, and supporting mobility. Despite facing challenges in security, service management, operation, and data handling, fog computing holds promise in sectors like agriculture, urban planning, and healthcare. This paper introduces an innovative machine learning algorithm designed to aid cloud platforms in selecting optimal scheduling strategies through multi-criteria decision-making, thereby improving performance optimization. Our primary objective is to minimize the makespan for a given set of tasks. To assess the effectiveness of our approach, we perform simulations using the CloudSim toolkit, examining the algorithm's performance across various configurations, including different numbers of Virtual Machines (VMs).