AI-Driven Service Architecture Optimization for Cloud-Native Big Data Platforms

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Published: 2026-05-24
Keywords:
AI-driven optimization, cloud-native architecture, big data platforms, service orchestration, scalability

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Wenbo Cheng
School of Computer and Information Engineering, Henan University of Technology, Zhengzhou, China
Yixuan Shi
School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China

Abstract

This research article explores the optimization of service architecture for cloud-native big data platforms using artificial intelligence (AI) techniques. The study focuses on leveraging AI-driven methodologies to enhance scalability, performance, and resource efficiency in distributed systems. A systematic approach is employed to analyze current challenges in cloud-native architectures, followed by the development of an AI-based framework for dynamic service orchestration. Experimental results demonstrate significant improvements in computational efficiency and workload distribution. The findings contribute to advancing the design of intelligent, adaptive systems for big data processing in cloud environments.

Article Details

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Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

AI-Driven Service Architecture Optimization for Cloud-Native Big Data Platforms. (2026). Hua Xia Xin Zhi, 2(1), 267-279. https://journals.hubblepress.com/index.php/HXXZ/article/view/47

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