Cloud-Native AI Applications Designing Resilient Network Architectures for Scalable AI Workloads in Smart Education
Sunil Kumar Reddy Jorepalli · Advances in educational technologies and instructional design book series · 2025
In the era of digital transformation, cloud-native AI applications have become integral to the evolution of smart education and sustainable learning environments. This chapter explores the design and implementation of resilient network architectures tailored for scalable AI workloads within these domains. It delves into the challenges of optimizing AI-driven educational platforms, addressing concerns such as network reliability, scalability, and cost-effectiveness. By leveraging cloud-native technologies, microservices, and edge computing, this chapter outlines strategies for creating flexible, scalable, and fault-tolerant systems that adapt to the dynamic demands of modern learning environments. Case studies from real-world applications in smart classrooms and online education platforms highlight the practical implications and benefits of these architectures. The chapter further emphasizes the role of sustainability in educational technology, focusing on energy-efficient infrastructure and cloud resource management to ensure environmentally responsible AI implementations.