Predicting Web service response time percentiles

Abstract

Predicting Web service response time percentiles is often an important aspect of service level management exercises. Existing techniques can be very time consuming since they involve the manual construction of complex analytic or simulation models. To address this problem, we propose Prospective, a fully automated and data-driven approach for predicting Web service response time percentiles. Prospective relies on historical response time data collected from a Web service. Given a specification for workload expected at the Web service over a planning horizon, Prospective uses this historical data to offer predictions for response time percentiles of interest. At the core of Prospective is a lightweight simulator that uses collaborative filtering to estimate response time behaviour of the service based on behaviour observed historically. Results show that Prospective is able to predict various response time percentiles of interest with high accuracy for a wide variety of workloads.