Deep Learning Applications in Service Performance Tuning for Distributed Big Data Platforms

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Published: 2026-05-24
Keywords:
Deep Learning, Service Performance Tuning, Distributed Systems, Big Data Platforms, Optimization

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Yujie Ma
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

Abstract

This research explores the application of deep learning techniques to optimize service performance in distributed big data platforms. By leveraging neural networks and advanced machine learning models, the study identifies critical parameters influencing system efficiency and proposes a novel framework for dynamic performance tuning. The methodology includes a combination of experimental simulations and real-world data analysis, focusing on latency reduction, throughput enhancement, and resource allocation optimization. Results demonstrate significant improvements in system performance metrics, validating the efficacy of the proposed approach. The discussion highlights the implications of these findings for large-scale data processing and future research directions in intelligent system management.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Deep Learning Applications in Service Performance Tuning for Distributed Big Data Platforms. (2026). Hua Xia Xin Zhi, 2(1), 231-241. https://journals.hubblepress.com/index.php/HXXZ/article/view/44

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