A service-agnostic method for predicting service metrics in real-time
(English)Manuscript (preprint) (Other academic)
We predict performance metrics of cloud services using statistical learning, whereby the behavior of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Our method is service agnostic in the sense that it takes as input operating-systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the 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.
Quality of service, cloud computing, network analytics, statistical learning, machine learning
Computer Systems Communication Systems Telecommunications
IdentifiersURN: urn:nbn:se:kth:diva-184203OAI: oai:DiVA.org:kth-184203DiVA: diva2:915578
QC 201604112016-03-302016-03-302016-04-11Bibliographically approved