ABSTRACT
The growing complexity of emergency response systems necessitates robust, scalable, and fault-tolerant architectures capable of supporting real-time analytics. Traditional monolithic and on-premise systems often struggle to meet the latency, availability, and elasticity demands required for critical decision-making during emergencies. This paper presents a conceptual model for cloud-native architectures tailored to enhance real-time analytics in emergency response environments. The proposed model leverages microservices, containerization, serverless computing, and distributed data processing frameworks to enable scalable and resilient operations. It integrates event-driven architecture (EDA) and stream processing to ensure continuous ingestion, processing, and analysis of large volumes of heterogeneous data from IoT devices, social media feeds, and sensor networks. A key component of the model is its reliance on fault-tolerant mechanisms such as container orchestration via Kubernetes, multi-zone deployments, and circuit breaker patterns, which together guarantee high availability and seamless failover capabilities. Furthermore, the architecture emphasizes the use of polyglot persistence and data lakehouse designs to accommodate structured and unstructured data while supporting AI and machine learning workloads for predictive insights. The model adopts a layered approach, encompassing data ingestion, stream processing, analytics, orchestration, and user interaction layers, ensuring modularity and ease of integration with existing emergency management platforms. Security, compliance, and data governance are embedded across all layers, addressing concerns around data privacy, integrity, and regulatory compliance. Through simulation scenarios and theoretical validation, the proposed architecture demonstrates its potential to enhance situational awareness, reduce response times, and improve decision-making accuracy during critical events such as natural disasters, pandemics, and infrastructure failures. This conceptual model provides a foundation for future implementation and research efforts aimed at operationalizing cloud-native paradigms in real-time, mission-critical domains.
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