Predicting Response Latency Percentiles for Cloud Object Storage Systems
Published in Proceedings of the 46th International Conference on Parallel Processing (ICPP), 2017
Recommended Citation: Yi Su, Dan Feng, Yu Hua, Zhan Shi, Predicting Response Latency Percentiles for Cloud Object Storage Systems, in the 46th International Conference on Parallel Processing (ICPP), 2017, pp. 241-250.
Abstract: As a fundamental cloud service for modern Web applications, the cloud object storage system stores and retrieves millions or even billions of read-heavy data objects. Serving for a massive amount of requests each day makes the response latency be a vital component of user experiences. Due to the lack of suitable understanding on the response latency distribution, current practice is to use overprovision resources to meet Service Level Agreement (SLA). Hence we build a performance model for the cloud object storage system to predict the percentiles of requests meeting SLA (response latency requirement), in the context of complicated disk operations and event-driven programming model. Furthermore, we find that the waiting time for being accept()-ed at storage servers may introduce significant delay. And we quantify the impacts on system response latency, due to requests waiting for being accept()-ed. In a variety of scenarios, our model reduces the prediction errors by up to 73% compared to baseline models, and the prediction error of our model is 4.44% on average.