A hybrid fuzzy logic and deep reinforcement learning algorithm for adaptive task scheduling and resource allocation in heterogeneous Fog–Cloud environments

Abstract

Intelligent task scheduling in distributed computing environments such as Fog–Cloud systems remains a significant challenge, particularly in the context of the Internet of Things (IoT), where multiple objectives such as minimizing delay, energy consumption, and makespan must be simultaneously addressed. This paper proposes an adaptive hybrid framework that integrates fuzzy logic with Deep Q-Network (DQN) reinforcement learning to optimize task scheduling and resource allocation in heterogeneous and dynamic environments. The model is designed to maintain service quality while remaining compatible with limited computational resources. The scheduling problem is first formulated as a multi-objective optimization model aimed at jointly minimizing delay, energy usage, and makespan. A fuzzy inference system is then employed to evaluate task attributes such as deadline, delay sensitivity, and data volume in order to assign priority levels. Based on this prioritization, the DQN agent dynamically allocates resources by interacting with the environment and learning from feedback. The proposed framework was evaluated on scenarios involving 500–2000 tasks under varying resource conditions, and its performance was benchmarked against conventional algorithms. Experimental results demonstrate that the proposed method achieves, on average, a 27.8 % reduction in execution time, a 29.6 % decrease in scheduling delay, an 18 % reduction in energy consumption, and a 21.4 % improvement in makespan. These outcomes highlight the framework’s effectiveness in balancing accuracy, responsiveness, and resource efficiency, making it well-suited for deployment in real-world, heterogeneous, and dynamically loaded computing environments.