Digital Twin for Indoor Farming Using Kubernetes Container-Based Application with Orchestration-Centric Performance Modeling

Indoor farming requires scalable, reliable, and low-latency monitoring and control to maintain optimal crop growth environmental conditions. Digital Twin (DT) technology offers a promising paradigm for virtually representing and managing physical farming systems. However, practical DT implementations for indoor agriculture often lack analytically grounded scalability models for edge-orchestrated DT systems. This study presents DTFarming, a microservices-based digital twin monitoring and control system for indoor farming deployed as a containerized service orchestrated using Kubernetes. Message Queuing Telemetry Transport (MQTT) is employed for event-driven telemetry exchange. This study proposes an analytical scalability model that relates sensor density to workload arrival rate, pod population, resource cost, memory saturation, and Kubernetes scheduling pressure. Experimental validation across three load scenarios (4, 20, and 100 sensors) confirms the predictive capability of the proposed model. The results indicate that the telemetry throughput scales linearly with minimal CPU overhead (<25%). However, memory is the primary bottleneck, capping scalability at ~110 pods on an 8GB edge node. This experimental saturation point aligns almost perfectly with our analytical model (112 pods), exhibiting a deviation of less than 2%. As deployments approach this limit, Kubernetes scheduling latency rises from 0.4 s to 2.1 s—a 5.25 times increase in the Scheduling Pressure Index (SPI)—while network latency and packet loss remain negligible. These findings confirm that orchestration overhead, memory constraints, dominates performance degradation rather than communication limits. The close agreement between model predictions and measurements confirms the model’s validity and offers practical guidance for sizing container-orchestrated DT platforms on constrained edge nodes.

Digital twin; Edge computing; Indoor farming; Internet of things (IoT); Microservices

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