Intelligent Processing of Complex Data and Architecture Scheduling Optimization in Cloud Environments

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Published: 2026-05-22
Keywords:
Cloud Computing, Data Processing, Scheduling Optimization, Machine Learning, Resource Allocation

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Zhetao Huang
Faculty of Computing, University of Portsmouth, Portsmouth, UK
Lars Eriksson
Faculty of Computing, University of Portsmouth, Portsmouth, UK

Abstract

This research article explores the intelligent processing of complex data and architecture scheduling optimization in cloud environments. The study introduces a novel framework that integrates advanced data processing techniques with optimized scheduling algorithms to enhance cloud computing performance. The methodology employs a combination of flow-based models and machine learning algorithms to address challenges such as resource allocation, task prioritization, and latency reduction. Results demonstrate significant improvements in computational efficiency, scalability, and cost-effectiveness under varying workload conditions. The discussion highlights the implications of these findings for real-world cloud applications and outlines potential avenues for future research.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Intelligent Processing of Complex Data and Architecture Scheduling Optimization in Cloud Environments. (2026). Hua Xia Xin Zhi, 2(1), 133-144. https://journals.hubblepress.com/index.php/HXXZ/article/view/35

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