Predicting service metrics for cluster-based services using real-time analytics
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, clientside 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 servicelevel 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.
Rerngvit Yanggratoke, Royal Institute of Technology, Sweden (KTH); Jawwad Ahmed, John Ardelius, Christofer Flinta, Andreas Johnsson, Ericsson Research; Daniel Gillblad, Rolf Stadler, Swedish Institute of Computer Science.
Published May 11, 2015, In Proceedings to the Network and Service Management Conference (CNSM)).
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