AN INTELLIGENT TASK SCHEDULING APPROACH FOR FOG COMPUTING

Abstract

By extending cloud computing to the network's edge, fog computing is a distributed computing paradigm that makes it possible to handle and analyze data in real time closer to its source. However, efficient task scheduling is necessary in fog computing optimize performance indicators such as latency, power consumption, and resource utilization. To overcome these difficulties, this study suggests Dynamic Scheduling Technique for Real-time Applications (DSTRA) based on reinforcement learning methods. The goal of the technique is to enhance the overall performance of fog computing systems by lowering latency and power consumption. Using real-time feedback from the fog nodes, DSTRA uses reinforcement learning to dynamically modify task priorities and resource allocation. With this strategy, the system can adjust to shifting circumstances and make the best scheduling choices possible in a dynamic environment. To ensure that latency-sensitive applications receive the necessary resources, tasks are prioritized based on their importance and deadline constraints. The DSTAR algorithm is evaluated through extensive simulations and real-world deployments, showing a 90% to 98% improvement in efficiency across key metrics including latency, power consumption, and overall system performance when compared to traditional scheduling approaches. This study addresses the critical resource needs of latency-sensitive applications by proposing a task-prioritization framework focused on importance and deadline constraints. We introduce the DSTRA algorithm, a robust solution for managing heterogeneous parallel task flows under dynamic constraints. DSTRA significantly outperforms conventional scheduling strategies. System delays are reduced by 90% to 98% Marked improvements are observed in power consumption and energy management, Overall resource allocation efficiency and system performance are substantially enhanced. The results confirm DSTAR’s efficacy in navigating complex, uncertain environments while maintaining optimal operational throughput.