Publication Details
Abstract
This study addresses job scheduling and resource allocation challenges in fog computing by utilizing Docker containers to implement an optimal priority scheduling method. Fog computing, which brings data processing closer to endpoints, enhances operational efficiency and reduces latency, extending the benefits of cloud computing. Docker's lightweight and scalable virtualization capabilities provide a stable framework for deploying and managing fog computing applications. The proposed algorithm optimizes task execution by dynamically prioritizing tasks based on their urgency and resource demands. In comparison to traditional scheduling methods such as round-robin and first-come-first-serve, the algorithm significantly reduces latency, improves task execution times, and maximizes resource utilization. Simulated experiments in fog environments with various IoT workloads show up to a 40% improvement in average latency and a 30% increase in resource utilization. These results demonstrate the efficacy of priority scheduling in addressing real-time application needs and resource limitations in fog environments. Furthermore, the study explores future optimization possibilities in fog computing systems through the integration of cutting-edge technologies like AI-driven predictive analytics. This research contributes to the growing body of knowledge in fog computing by offering a practical and scalable approach to managing Internet of Things (IoT) applications. It provides valuable insights for researchers and practitioners aiming to enhance the efficiency of distributed computing systems.