Cloud-Native Application Development Using Spring Boot
Abstract
Cloud-native application development has emerged as a transformative paradigm for building scalable, resilient, and highly available enterprise systems in modern computing environments. This paper explores the integration of Spring Boot, Pivotal Cloud Foundry, and AI-driven auto-scaling techniques to design and deploy intelligent cloud-native applications. Spring Boot simplifies the development of microservices by providing lightweight, production-ready configurations and seamless integration with enterprise ecosystems, enabling rapid application development and deployment. Pivotal Cloud Foundry offers a robust Platform-as-a-Service (PaaS) environment that supports continuous delivery, automated deployment, and efficient resource management, allowing developers to focus on application logic rather than infrastructure concerns. However, traditional scaling mechanisms in cloud environments are often reactive, relying on predefined thresholds that may not adapt effectively to dynamic workloads. To address this limitation, the proposed framework incorporates AI-driven auto-scaling mechanisms that utilize machine learning algorithms to analyze historical and real-time system metrics, predict workload patterns, and dynamically allocate resources. By leveraging techniques such as time-series forecasting, anomaly detection, and reinforcement learning, the system can proactively scale applications based on anticipated demand, reducing latency, improving performance, and optimizing resource utilization
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