Cloud-Native System Architecture for Massive Uncertain Disaster Data Processing

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Published: 2026-05-24
Keywords:
cloud-native architecture, disaster data processing, microservices, serverless computing, real-time systems

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Haoan Qu
School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
Ziyuan Meng
Faculty of Engineering, Cebu Technological University, Cebu, Philippines
Jiankun Tian
Faculty of Informatics, University of Debrecen, Debrecen, Hungary
Maximilian Vester
Faculty of Engineering, Cebu Technological University, Cebu, Philippines
Gunter Scheuer
Faculty of Informatics, University of Debrecen, Debrecen, Hungary

Abstract

This research article explores the design and implementation of a cloud-native system architecture tailored for processing massive and uncertain disaster data. The study addresses the challenges posed by the unpredictable nature of disaster-related data, including its heterogeneity, volume, and real-time processing requirements. A novel architecture leveraging microservices, containerization, and serverless computing is proposed, enabling scalable, resilient, and efficient data handling. The methodology includes the development of a prototype system tested under simulated disaster scenarios, with performance metrics such as latency, throughput, and fault tolerance analyzed. Results demonstrate significant improvements in processing efficiency and system adaptability compared to traditional monolithic architectures. The discussion highlights the implications of the findings for disaster management systems and outlines future research directions.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Cloud-Native System Architecture for Massive Uncertain Disaster Data Processing. (2026). Hua Xia Xin Zhi, 2(1), 218-230. https://journals.hubblepress.com/index.php/HXXZ/article/view/43

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