Collaborative Optimization of Cloud System Architecture for High-Concurrency Big Data Computing

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Published: 2026-05-24
Keywords:
Cloud System Architecture, High-Concurrency, Big Data Computing, Optimization, Scalability

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Haoran Tao
Department of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
Chunhua Bai
School of Information Engineering, Shaanxi University of Technology, Hanzhong, China
Yunhan Qiu
College of Computer Science, Chongqing University of Science and Technology, Chongqing, China
Minghui Tang
School of Electronic Engineering, Hubei University of Technology, Wuhan, China

Abstract

This research article explores the collaborative optimization of cloud system architectures tailored for high-concurrency big data computing. The study introduces a systematic methodology to enhance scalability, fault tolerance, and real-time processing capabilities in cloud environments. By employing advanced architectural frameworks and experimental evaluations, the research identifies critical parameters influencing system performance under high-concurrency workloads. Results demonstrate significant improvements in throughput, latency, and resource utilization, offering actionable insights for cloud architects and engineers. The findings aim to bridge the gap between theoretical models and practical implementations, ensuring robust and efficient cloud systems for big data applications.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Collaborative Optimization of Cloud System Architecture for High-Concurrency Big Data Computing. (2026). Hua Xia Xin Zhi, 2(1), 207-217. https://journals.hubblepress.com/index.php/HXXZ/article/view/41

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