Fighting with Unknowns: Estimating the Performance of Scalable Distributed Storage Systems with Minimal Measurement Data

Constructing an accurate performance model for distributed storage systems has been identified as a very difficult problem. Researchers in this area either come up with an involved mathematical model specifically tailored to a target storage system or treat each storage system as a black box and apply machine learning techniques to predict the performance. Both approaches involve a significant amount of efforts and data collection processes, which often take a prohibited amount of time to apply to real world scenarios. In this paper, we propose a simple, yet accurate, performance estimation technique for scalable distributed storage systems. We claim that the total processing capability per IO size is conserved across a different mix of read/write ratios and IO sizes. Based on the hypothesis, we construct a performance model which can be used to estimate the performance of an arbitrarily mixed IO workload. The proposed technique requires only a couple of measurement points per IO size in order to provide accurate performance estimation. Our preliminary results are very promising. Based on two widely-used distributed storage systems (i.e., Ceph and Swift) under a different cluster configuration, we show that the total processing capability per IO size indeed remains constant. As a result, our technique was able to provide accurate prediction results.