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.
Edge computing has emerged as a foundational paradigm for intelligent digital infrastructure because it reduces
latency, improves bandwidth utilization, and enables real-time analytics close to data sources. Yet modern edge
environments remain highly volatile. Resource availability changes continuously. IoT traffic fluctuates unpredictably.
Mobile users migrate across heterogeneous networks. Conventional heuristic-based schedulers struggle to maintain stable
Quality of Service (QoS) under such conditions. Deep Reinforcement Learning (DRL) offers an adaptive decision-making
framework capable of learning dynamic resource allocation strategies directly from complex environments. This paper
investigates adaptive edge resource management through DRL-driven optimization models for computation offloading, task
scheduling, bandwidth allocation, energy efficiency, and autonomous orchestration in distributed edge ecosystems. The
study synthesizes recent advances between 2020 and 2025 across edge intelligence, federated learning, multi-agent
reinforcement learning, and AI-driven autonomous networking. A layered DRL-enabled edge orchestration framework is
proposed to optimize latency, throughput, energy consumption, and load balancing simultaneously. The research also
formulates two research questions focused on scalability and adaptive scheduling under heterogeneous workloads. The
proposed methodology integrates Proximal Policy Optimization (PPO), Deep Q-Networks (DQN), Multi-Agent Deep
Deterministic Policy Gradient (MADDPG), and federated reinforcement learning within a cloud-edge continuum.
Comparative analysis indicates that DRL-based adaptive management substantially improves response latency, energy
utilization, and computational efficiency compared with static and rule-based schedulers. The paper identifies unresolved
challenges involving reward engineering, explainability, convergence stability, privacy preservation, and large-scale
deployment in 6G-enabled edge systems. 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
Task scheduling plays a crucial role in optimizing performance, reducing costs, and enhancing system reliability by efficiently allocating resources to workloads. Traditional task scheduling methods lack the ability to efficiently manage workloads and resource distribution, leading to potential inefficiencies in performance and energy consumption. To address these limitations, advanced techniques leveraging deep learning and reinforcement learning are explored. This study proposes a deep learning-based model for task scheduling in cloud computing. The model employs a convolutional neural network (CNN) to predict the optimal machines for task allocation. Additionally, Q-learning is integrated with CNN to facilitate load shifting between machines, ensuring efficient utilization of resources. The dataset used in this work consists of task attributes, such as execution time, resource requirements, which were loaded from a CSV file. Comparative analysis with existing models shows that the proposed approach achieves approximately 94% accuracy and consumes less energy than other models, demonstrating its effectiveness in cloud task scheduling.
Kavita Rani, O. Sangwan, R. Garg· IAES International Journal o...· 0 citations
Cloud computing enables on-demand access to scalable virtualized resources. However, efficient task scheduling in cloud computing remains a challenge because of the dynamic and heterogeneous nature of workloads. This paper proposes a hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network (DQN), Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) to enable adaptive resource scheduling in cloud environments. The proposed model is implemented using PyTorch and evaluated in a CloudSim based simulation environment Experimental results show that the proposed approach achieves a consistent improvement in terms of makespan reduction and VM utilization compared to individual DRL approaches and classical scheduling algorithms. Experiments were reiterated with multiple runs to ensure reliability and statistical measures are reported. Under the evaluated conditions, the proposed approach shows more efficient scheduling performance, but it has higher computational overhead and is only validated in a simulated environment for now. These results suggest that hybrid DRL-based scheduling is a promising approach for adaptive cloud resource management, with potential for further validation in real-world deployments and energy-aware scenarios.
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.