Document Type

Article

Publication Date

2025

DOI

10.1007/s11235-025-01332-9

Publication Title

Telecommunication Systems

Volume

88

Issue

3

Pages

105

Abstract

Fog Radio Access Network (Fog RAN) has recently emerged as a promising architecture for supporting low-latency applications by bringing fog nodes and cloud resources closer to end users. However, existing research on computational offloading in Fog RAN lacks a comprehensive framework that addresses three key aspects: where to offload tasks, which processing nodes to utilize, and how to allocate resources for tasks with varying latency requirements. To address this gap, we propose TOFRA (Task Offloading for Fog RAN), a novel latency-aware task offloading framework. TOFRA is a centralized system that determines the optimal offloading strategy, whether to execute tasks locally, at fog nodes, or in the cloud, selects the appropriate processing servers, and intelligently allocates both radio and computation resources based on task priorities. We employ a Genetic Algorithm (GA) to solve this optimization problem, as it effectively handles large-scale and complex system scenarios. Simulation results demonstrate that TOFRA significantly reduces latency, particularly by minimizing maximum latency for high-priority tasks compared to existing approaches, while maintaining acceptable performance for lower-priority tasks. The algorithm also exhibits fast convergence, typically stabilizing within 25 iterations, even in networks with up to 210 users. In summary, TOFRA represents a substantial advancement in task offloading strategies for Fog RAN systems across diverse network loads. Future work will extend the framework by incorporating factors such as packet loss, dynamic network failures, and multi-objective optimization, including energy consumption and bandwidth efficiency, enhancing its robustness and applicability in real-world deployments.

Rights

© The Authors 2025

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "No datasets were generated or analysed during the current study."

Original Publication Citation

Taheri, S., Moghim, N., Movahhedinia, N., & Shetty, S. (2025). A framework for task offloading in heterogeneous computing applications within the Fog RAN architecture. Telecommunication Systems, 88(3), Article 105. https://doi.org/10.1007/s11235-025-01332-9

ORCID

0000-0002-8789-0610 (Shetty)

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