An Adaptive Mini-Batching Strategy for Reliable Streaming Data Delivery in Real-Time

Modern applications show an increasing demand for continuously processing massive data streams in real time. Mini-batching technique is commonly used for transporting streaming data across these applications. However, although a larger mini-batch size increases throughput, it also raises the end-to-end latency and easily violate the latency constraint required by real-time applications. While existing work mostly studies this problem at the computation stage, this work explores it in the streaming data transportation stage. We show that selecting a proper mini-batch size is essential for efficient streaming data delivery. We further identify the shortcomings of existing latency measurements and introduce new Quality of Service (QoS) metrics: latency violation rate, timely throughput, message loss, and duplicate rate. To address the challenge of mini-batching under varying network conditions, we first develop prediction models for the proposed QoS metrics and then adaptively adjust the mini-batch size based on these predictions. In our experiments, the random forest regressor achieves anR²of 0.99 for performance metrics, and the multilayer perceptron achieves an MAE below 0.02 for reliability metrics. Using these predictions, the proposed adaptive strategy continuously updates the mini-batch size according to the observed network state. In a Kafka testbed with network packet loss rate approaching 25%, our strategy improves timely throughput by up to 30% compared with empirical mini-batch size selection. The results confirm the effectiveness of the adaptive mini-batching approach.