Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-7· 0 citations· 18 references
Abstract
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.
A task scheduling method using the Deep Q-Network to determine the computation node for the computation task and a dynamic congestion-aware mechanism to determine a low-cost routing path is proposed, which gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations.
Dynamic edge networks suffer from fluctuating bandwidth, latency, and edge-node load, which weaken conventional task offloading strategies under deadline and energy constraints. This study proposes an adaptive task offloading strategy based on dynamic network states. An online broad learning system predicts short-term...
Jie Yang, Peng Li, Guangfu Ge et al.· 2026 5th International Confe...· 0 citations
A Mixed Integer Nonlinear Programming (MINLP) model with the objective of a weighted sum of long-term average task completion rate, total latency and energy consumption is established, which improves the task completion rate by 4% in high load scenarios and achieves a better balance between latency and energy consumpti...
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.
Sophia M. Carter, Rohan V. Iyer, Emilio J. Navarro· Journal of Computer Science· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system pote...
Mamoon M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The...
Xiaoteng Yang, Jie Feng, Lei Liu et al.· IEEE transactions on compute...· 0 citations
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