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All Journal The Indonesian Journal of Computer Science
Abdulwahab, Sara
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comprehensive Review of Advanced Machine Learning Strategies for Resource Allocation in Fog Computing Systems Abdulwahab, Sara; Ibrahim, Media; Askar, Shavan; Hussien, Diana
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4632

Abstract

This paper targets the development of advanced machine learning strategies for fog computing systems and is designed to further enhance current mechanisms related to resource allocation. Fog computing represents the extension of cloud facilities to network edges with increased data processing, allowing minimal latency for applications that need real-time processing. This is a review underlining deep learning as one of the basic tools through which neural networks predict the resource usage and optimization of resource allocation with its dynamic adaptation to modifications within the network conditions. The paper reviews techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks that are explored for their roles in enhancing efficiency, privacy, and responsiveness within the realm of distributed environments. These findings reveal that deep learning significantly enhances operational performance, reduces latency, and strengthens security in fog networks. By processing data locally and autonomously managing resources, these strategies ensure efficient handling of diverse and dynamic demands. It concludes that the integration of machine learning into fog computing forms a scalable and robust framework toward meeting modern challenges imposed by digital ecosystems, enabling smarter real-time decision-making systems at the edge.
Deep Learning for Dynamic Resource Management in 5G Networks: A Review Diana Hayder Hussein; Abdulwahab, Sara; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4688

Abstract

Dynamic resource management is important for 5G wireless networks to ensure they are efficient, scalable, and can handle growing connectivity demands while maintaining quality service. The aim of this review is to discuss how deep learning has changed the way complex challenges are being addressed in resource allocation, frequency spectrum management, energy efficiency, and runtime decision-making over 5G wireless networks. It combines the very best of leading-edge research insights into showing, through advanced deep learning techniques like supervised learning, and federated learning, how to allow for intelligent, adaptive solutions that go beyond conventional approaches. The manuscript describes this through a review that compares the strengths of these methodologies in network performance optimization while pointing out some limitations related to computational complexity or lack of extensive real-world testing. It further elaborates on promising future directions, ranging from federated learning for decentralized resource management to enhancing the interpretability of deep learning models and leveraging diverse datasets for improving robustness. The discussion also covers the arrival of 6G networks, which will introduce refined and AI-driven approaches for resource optimization. By establishing the logical links between theoretical developments and practical uses, the presented review will pinpoint the transforming potential of deep learning in re-shaping both the wireless communication networks of the future, but also opening new frontiers well beyond 5G.
Co-Authors askar, shavan Diana Hayder Hussein Hussien, Diana Ibrahim, Media Media Ali Ibrahim Shavan Askar