Speal: Achieving a More Accurate Model with Less Training Data in Performance Evaluation of Storage System through Sampling Optimization

Performance evaluation, as a crucial component of Quality of Service (QoS), holds significant importance for modern storage systems. Previous machine learning-based methods ignore the varied improvements in the model after applying different datasets for training. Suboptimized random sampling methods may lead to the collection of unnecessary training data, resulting in excessively high dataset construction costs. This problem becomes more pronounced when there are constraints on the sampling and storage system resources. In this paper, we propose Speal, Storage System Performance Evaluator with Active Learning, which utilizes machine learning to predict the performance of the workload running on the storage system. We present a straightforward yet highly effective active learning algorithm called E2 sampling, employed during the model construction phase to reduce the cost of training dataset acquisition. Furthermore, we apply Speal to the storage system to facilitate bandwidth control and optimize performance. In our experiments using performance data collected from the real storage system, Speal exhibits up to 1.75x reduction in prediction error compared to other active learning algorithms. Additionally, implementing the bandwidth control enhanced by Speal ’s performance evaluation to the storage system leads to an average throughput improvement of up to 1.51x and a reduction in tail latency by up to 1.71x, surpassing the baseline.