# Optimized Cache Placement in Edge Computing Using Machine Learning-Based Predictive Models
> Mustofa Y.A.

URL kanonis: https://discover.unhas.ac.id/publications/optimized-cache-placement-in-edge-computing-using-machine-learning-based-predict
Jurnal / Konferensi: Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/ICOEI65986.2025.11013214
Citations: 0

## Authors
- Mustofa Y.A.

## Abstract
Cache placement in edge computing is crucial in optimizing data retrieval efficiency, particularly in environments characterized by fluctuating content demand and constrained storage capacity. This study proposes a machine learning-driven approach to enhance cache placement decisions, employing Logistic Regression and Support Vector Machines as predictive models. To refine classification accuracy and computational efficiency, the models are optimized through gradient-based techniques such as Stochastic Gradient Descent, Mini-Batch Gradient Descent, Momentum Gradient Descent, Adam, and Newton's Method. The methodology involves processing real-world access data, where key features such as response time, access frequency, and file size inform caching decisions. Experimental evaluations reveal that Newton's Method delivers the highest classification accuracy 97.5% with rapid convergence, while Momentum Gradient Descent achieves the highest cache hit rate 13% at a cache size of 150 MB. Furthermore, the proposed machine learning models significantly surpass traditional caching strategies such as Least Recently Used and Least Frequently Used, achieving up to a 100% improvement in cache hit rate. These findings underscore the effectiveness of adaptive, data-driven caching techniques in enhancing network performance and reducing latency in real-time edge computing environments.

## Keywords
- Computer science
- Cache
- Enhanced Data Rates for GSM Evolution
- Parallel computing
- Cache algorithms
- Edge computing
- Artificial intelligence
- Machine learning
- CPU cache

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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