Predicting service metrics for cluster-based services using real-time analytics
2015 (English)In: IFIP/IEEE 11th International Conference on Network and Service Management, CNSM 2015, Barcelona, Spain, November 9-13, 2015, IEEE conference proceedings, 2015Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2015.
Quality of service, cloud computing, network analytics, statistical learning, machine learning
Communication Systems Computer Systems Telecommunications Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-172795DOI: 10.1109/CNSM.2015.7367349ISI: 000379333700019ScopusID: 2-s2.0-84964055190OAI: oai:DiVA.org:kth-172795DiVA: diva2:849585
IFIP/IEEE 11th International Conference on Network and Service Management, CNSM 2015, Barcelona, Spain, November 9-13, 2015
QC 201510022015-08-282015-08-282016-08-12Bibliographically approved