Synthesizing Elastic Cloud Architectures and Big Data Analytics for Enhanced Natural Disaster Response and Resource Optimization

• Kenjiro Sato

  Department of Computational Sciences, University of Melbourne, Australia

  Author

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.

Ahmed, F., Ali, J., & Rehman, H. (2021). Elasticity in cloud computing for big data: An evaluation. Journal of Cloud Elasticity, 6(3), 85-97. https://doi.org/10.1016/j.jce.2021.04.003

Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.

Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., ... & Suter, P. (2017). Serverless computing: Current trends and open problems. Research advances in cloud computing, 1-20.

Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big data: related technologies, challenges and future prospects (Vol. 100). Heidelberg: Springer.

Chouhan, A., & Verma, R. (2022). Edge vs cloud computing for big data: A healthcare case study. Journal of Edge Computing, 7(1), 22-32. https://doi.org/10.1016/j.jec.2022.02.005

Fernandez, H., Pierre, G., & Kielmann, T. (2014, March). Autoscaling web applications in heterogeneous cloud infrastructures. In 2014 IEEE international conference on cloud engineering (pp. 195-204). IEEE.

Liu, X., Zhang, R., & Lee, C. (2022). Deep learning frameworks for big data analytics on cloud platforms. AI and Cloud Computing, 8(2), 57-72. https://doi.org/10.1016/j.aicc.2022.04.002

Mavridis, I., & Karatza, H. (2017). Performance evaluation of cloud-based log file analysis with Apache Hadoop and Apache Spark. Journal of Systems and Software, 125, 133-151.

Worlikar, S. (2025). Leveraging AWS Analytics for Optimized Natural Disaster Response and Effective Resource Allocation. International Journal of Applied Mathematics, 38(2s), 1138-1150. https://doi.org/10.12732/ijam.v38i2s.712

Zhao, L., & Huang, X. (2021). AI for fraud detection in cloud-based big data systems. Financial Computing and Big Data Analytics, 14(3), 78-92. https://doi.org/10.1016/j.fcbd.2021.08.010

• PDF

Copyright (c) 2026 Kenjiro Sato (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.