The increasing volume and variability of IoT-generated data demand blockchain infrastructures capable of dynamic scaling while maintaining performance guarantees. This study presents a Kubernetes-based framework for deploying and evaluating private blockchain networks under fluctuating IoT workloads derived from real-world devices. The architecture integrates containerized blockchain services with horizontal pod autoscaling, enabling systematic investigation of capacity adaptation strategies in distributed ledger systems. Data streams originating from IoT sensors, including telemetry and energy monitoring measurements, generate realistic variable-intensity workloads for experimental evaluation. The framework supports quantitative analysis of throughput-latency trade-offs, resource utilization patterns, and system resilience under traffic variability. Two autoscaling policies are compared: a conservative configuration limiting horizontal expansion and an aggressive configuration permitting broader capacity scaling.

Results show that expanded scaling capacity reduces mean response latency by 19% and tail latencies by 23-24%, at the cost of reduced throughput due to orchestration overhead. Both configurations maintain above 98% reliability under sustained high-concurrency conditions, demonstrating robust operation across the evaluated scaling range. Findings reveal fundamental trade-offs between responsiveness and resource efficiency in autoscaled blockchain deployments. The framework provides a reproducible methodology for benchmarking blockchain performance under real IoT workloads, contributing to the design of adaptive distributed ledger infrastructures balancing scalability, latency, and operational efficiency.