Open Access
Synthesizing Elastic Cloud Architectures and Big Data Analytics for Enhanced Natural Disaster Response and Resource Optimization
4
Department of Computational Sciences, University of Melbourne, Australia
Abstract
The rapid escalation of global climate volatility has necessitated the development of highly responsive and scalable computational frameworks to manage natural disasters. This research investigates the intersection of elastic cloud computing, big data analytics, and artificial intelligence (AI) as a tripartite solution for optimizing disaster response and resource allocation. By synthesizing contemporary advancements in serverless computing, edge-to-cloud continuums, and deep learning frameworks, this study provides a comprehensive blueprint for real-time crisis management. We explore the role of Apache Hadoop and Spark in processing massive log files and sensor data, the implications of heterogeneous cloud infrastructures on autoscaling, and the deployment of AI for fraud detection and financial integrity during emergency aid distribution. Central to this analysis is the integration of Amazon Web Services (AWS) analytics to facilitate precision in logistical operations. The study reveals that the combination of elasticity and edge computing significantly reduces latency in healthcare delivery during emergencies while maintaining high data throughput. This article elaborates on the theoretical paradigms of cloud elasticity, the architectural challenges of heterogeneous resource provisioning, and the socio-technical implications of AI-driven disaster mitigation.
Keywords
Cloud Computing,
Big Data Analytics,
Natural Disaster Response,
Resource Allocation
References
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How to Cite
Kenjiro Sato. (2026). Synthesizing Elastic Cloud Architectures and Big Data Analytics for Enhanced Natural Disaster Response and Resource Optimization. Global Multidisciplinary Journal, 5(01), 161-165. https://www.grpublishing.org/journals/index.php/gmj/article/view/354
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