Efficient scheduling algorithm for optimizing system load in fog computing environment: A fuzzy reinforcement learning mechanism

New technologies have emerged over the last few years, such as IoT and fog computing. IoT devices and the enormous amounts of data generated every minute have led to the vast growth of the Internet of Things (IoT). In order to meet the term “Data Never Sleeps”, some IoT applications require real-time services and low bit latency. To provide quick processing, storage, and services, Cisco proposed fog computing as an extension of cloud computing. The traditional methods are not capable of addressing the complex scheduling scenarios of fog computing. In this paper, we introduce a novel Fuzzy Reinforcement Learning Scheduling algorithm (FRLS) that enhances schedule accuracy in dynamic computing environments. To optimize task scheduling, the FRLS algorithm integrates fuzzy logic with reinforcement learning. To prioritize critical tasks, fuzzy logic handles uncertainty and prioritizes tasks according to deadlines, sizes, and file sizes. Then, reinforcement learning schedules the prioritized tasks, continually adjusting to dynamic conditions to ensure the best resource allocation. In addition to improving overall system performance, this combination provides a robust framework that can address the complexity and variability of fog computing environments. FRLS is designed to minimize response time while adhering to resource and deadline constraints in fog-based applications. A comparison of FRLS with existing algorithms shows that it significantly improves load balancing, deadline satisfaction, response time, and waiting time. Combining reinforcement learning and fuzzy logic leads to an efficient scheduling solution. In addition, FRLS outperforms non-prioritized algorithms.