Optimizing Task Scheduling in Fog Computing with Deadline Awareness

Abstract: The rise of Internet of Things (IoT) devices has led to the development of numerous applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. A well-designed job scheduling algorithm can help decrease energy usage and improve response times for application requests. This work aims to schedule tasks in IoT while minimizing the total energy consumption of nodes and enhancing the Quality of Service (QoS) requirements of IoT tasks, taking into account task deadlines. Initially, this paper classifies the Fog nodes into two categories based on their traffic level: low and high. It schedules low-deadline tasks on low-traffic-level nodes using an Improved Golden Eagle Optimization (IGEO) algorithm, an enhancement of the Golden Eagle Optimization Algorithm that utilizes genetic operators for discretization. High-deadline tasks are processed on high-traffic nodes using reinforcement learning (RL). This combined approach is called the Reinforcement Improved Golden Eagle Optimization (RIGEO) algorithm. Experimental results demonstrate that the proposed algorithms optimize system response time, total deadline violation time, and resource and system energy consumption compared to other state-of-the-art algorithms.