A Microservices-Based Hybrid Cloud-Edge Architecture for Real-Time IIoT Analytics

Abstract

Hybrid and multi-cloud computing have become very important for enterprises and companies managing large-scale, real-time data streaming and analytics. At the same time, edge computing has come into the limelight as a key approach to reducing latency and optimizing resource usage at the network’s outermost layer.
This paper bridges the gap between scalable multi-cloud architectures and modern microservices-driven cloud-edge collaboration which solves real-time streaming, analytics, and condition monitoring. We first summarize the major limitations—such as data integration, latency, interoperability, and vendor lock-in—when designing solutions that span in different cloud environments.
We then provide a hybrid architecture that integrates edge computing to guarantee quick response and uses microservices for containerized deployments. For activities like feature extraction and AI-driven analytics, we will investigate cloud-edge collaboration solutions that will allow for instant decision-making at the edge while also shifting more complicated processing to the cloud.
Lastly, we present a case study on battery scanning and quality analysis in battery manufacturing. Findings show that integrating multi-cloud and edge computing can reduce operating expenses, cut latency by up to 30%, and greatly increase predictive accuracy, with a 90% success rate in real-time anomaly detection and a 50% reduction in predictive mistakes.
Finally, this hybrid cloud-edge strategy positions itself as a strong framework for the upcoming generation of intelligent applications by improving scalability, real-time efficiency, and cost-effectiveness.