Predicting service metrics for cluster-based services using real-time analytics - Yanggratoke, Rerngvit; Ahmed, Jawwad; Ardelius, John; Flinta, Christofer; Johnsson, Andreas; Gillblad, Daniel; Stadler, Rolf (2015 11th International Conference on Network and Service Management (CNSM), 2015)
Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.