EdgeSched-DQN: An intelligent deep reinforcement learning-based framework for optimized task scheduling in edge-cloud environments
Nagendar Yamsani, Pakanati Chenna Reddy · Array · 2025
Edge-cloud computing is providing a fundamental for low-latency and resource-intensive Internet of Things (IoT) applications by creating a green ecosystem for task scheduling and resource utilization. That said, these approaches are not able to efficiently handle dynamically varying workloads, do not place resources optimally and also may not scale in an unpredictable environment. Heuristic-based methods are inflexible, conventional machine learning approach is static, while traditional reinforcement learning (RL) suffers from the issues of action space complexity and real-time deployment. To overcome these constraints, we propose a new Deep Q-Network (DQN)-based task scheduling framework, EdgeSched-DQN. An adaptive pruning principle which minimizes action space dimensionality and balances resource allocation with desired levels of system balance, and efficiency, has been integrated into the framework. An optimal task execution, which enforces latency constraints, can be achieved by a tailored reward function that accommodates changing workloads on-the-fly. Abstract It can achieve a 25 % higher reward, 20 % shorter response time, and 18 % higher success rate than state-of-the-art methods. These numbers prove the capability of EdgeSched-DQN as a competent and efficient approach for task scheduling in latency-sensitive applications. It is real-time aware when applied in IoT ecosystems, smart cities and autonomous systems to utilize limited resources more efficiently in edge-cloud environments.