Jun 2026· Journal of Computer Science· Vol 02, pp. 05-08· 0 citations
TL;DR
This paper proposes an Energy-Efficient Deep Reinforcement Learning (EE-DRL) framework that optimizes task scheduling while minimizing energy consumption and execution delay, and employs a Deep Q-Network to dynamically allocate computational tasks among heterogeneous edge nodes.
Abstract
Edge computing has emerged as an effective paradigm for processing latency-sensitive applications by bringing computational resources closer to end users. However, efficient task scheduling remains a significant challenge due to limited computational capacity, dynamic workloads, and varying network conditions. This paper proposes an Energy-Efficient Deep Reinforcement Learning (EE-DRL) framework that optimizes task scheduling while minimizing energy consumption and execution delay. The proposed framework employs a Deep Q-Network (DQN) to dynamically allocate computational tasks among heterogeneous edge nodes. Experimental analysis demonstrates that the proposed approach reduces average task completion time by 23%, lowers energy consumption by 19%, and improves resource utilization compared with traditional scheduling algorithms. The proposed framework provides an intelligent, adaptive, and scalable solution for future edge computing infrastructures.
The findings demonstrate that DRL-driven adaptive orchestration can become a central mechanism for autonomous edge intelligence in next-generation AI-native communication infrastructures.
Amit K. Mogal, Rahul A. Patil, Sahebrao N. Shinde et al.· International Journal of Inn...· 0 citations
Experimental results demonstrate that DL-EATS achieves the lowest energy consumption, shortest makespan, minimal SLA violation rate, and highest resource utilization, representing an 18.5% improvement in energy efficiency over the next best method and substantial gains across all performance metrics.
Abdulmumini Adamu, A. A. Abdulwasiu· Journal of Science Research...· 0 citations
A deep learning-based model for task scheduling in cloud computing that employs a convolutional neural network to predict the optimal machines for task allocation and consumes less energy than other models is proposed, demonstrating its effectiveness in cloud task scheduling.
Kavita Rani, O. Sangwan, R. Garg· IAES International Journal o...· 0 citations
A hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network, Proximal Policy Optimization and Advantage Actor-Critic to enable adaptive resource scheduling in cloud environments is proposed.
P. Priya, J. Geetha, E. Naresh et al.· International Journal of Com...· 0 citations
An AI-enabled dynamic task scheduling framework based on Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) model to dynamically assign tasks to virtual machines and learn the best scheduling policies by continuously interacting with the cloud environment based on system parameters such as resource availability, task queue length, and virtual machine load is introduced.
Karnam Sreenu, G. Prasadu, K. Premnadh et al.· VFAST Transactions on Softwa...· 0 citations
Traditional resource scheduling strategies fail to fully utilize the computing and storage resources of edge nodes, leading to resource waste and overload of some nodes. This paper collects network topology and edge node resource information data for preprocessing. Then, a state space and action space are defined to record all possible system states and scheduling decisions in the edge computing environment. A two-layer deep Q-network model is constructed for action selection and Q-value calculation. Using 10-fold cross-validation and averaging, the optimized resource utilization reaches 85.18%, communication latency is $\mathbf{7 9. 1 m s}$, load balancing is improved to 0.841, and energy consumption and rejection rate are reduced to varying degrees, fully demonstrating the advantages of this algorithm in optimizing communication network resource scheduling in edge computing environments.