Scalable and Resilient Microservices Deployment for Edge Computing with Kubernetes

Edge computing is increasingly critical for processing data closer to the source, minimizing latency, and improving performance for various real-time applications. This work explores deploying scalable and resilient microservices architectures in edge environments using Kubernetes. Leveraging the publicly available EdgeDiver Dataset, the study demonstrates how Kubernetes orchestrates microservices effectively, ensuring scalability, load balancing, and fault tolerance across multiple edge nodes. This paper highlights novel findings such as enhanced fault recovery mechanisms, improved resource allocation, and scalability benchmarks under dynamic workloads. These contributions underscore Kubernetes' potential as a foundational technology for edge-native applications in domains such as autonomous vehicles, smart cities, and real-time analytics.