Distributed Event-Driven Serverless Platform for Multicluster IoT Environments

In modern smart city and IoT environments, diverse sensors for traffic management, environmental monitoring, and energy systems continuously generate large volumes of heterogeneous events in real time. Efficiently processing these multi-source event streams requires a scalable and responsive computing architecture. However, many Kubernetes-hosted serverless Function-as-a-Service (FaaS) deployments operate within a single administrative cluster and provide limited user-level control over dynamic multicluster placement based on heterogeneous event types and real-time resource conditions. To address these limitations, this study proposes a generalized event-driven FaaS architecture capable of efficiently processing multi-event streams across multicluster environments. The proposed architecture was implemented on Kubernetes-based testbed by integrating a multicluster orchestrator, an event-processing engine, a workflow execution layer, and a serverless platform. Evaluation using a smart city-inspired scenario demonstrates that the proposed platform provides improved load distribution characteristics and maintains higher workflow success rates under increasing workloads compared to the evaluated single-cluster baseline. This research provides a scalable design approach for serverless platforms that can meet real-time event processing requirements in IoT and smart city applications.