DIST: Distributed Learning-Based Energy-Efficient and Reliable Task Scheduling and Resource Allocation in Fog Computing
Elyas Oustad, Abolfazl Younesi, Mohsen Ansari, Sepideh Safari, Mohammad Arman Soleimani, Jörg Henkel, Alireza Ejlali · IEEE Transactions on Services Computing · 2025
This paper presents DIST, a novel distributed reinforcement learning-based (DRL) framework for energyefficient and reliable task scheduling and resource allocation in fog computing, low-latency computing solutions driven by the rapid deployment of IoT devices, and time-sensitive applications. DIST is built based on a novel distributed Q-learning to enable fog nodes to learn an optimal strategy to balance energy consumption, task execution time, and system reliability. The main novelty includes a cooperative Dynamic Voltage and Frequency Scaling-enabled task scheduling policy that dynamically adjusts node energy level to ensure power consumption reduction without sacrificing deadline adherence or reliability. The results demonstrate that DIST reduces energy consumption by up to 52.26%, realizes 38% higher success rates, and reduces task wait times by up to 46.77%, compared with state-of-the-art algorithms.