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A service-agnostic method for predicting service metrics in real time
KTH Royal Institute of Technology, Sweden.
Ericsson Research, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.
Ericsson Research, Sweden.
Vise andre og tillknytning
2018 (engelsk)Inngår i: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, nr 2, artikkel-id e1991Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We predict performance metrics of cloud services using statistical learning, whereby the behaviour 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. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, 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. 

sted, utgiver, år, opplag, sider
2018. Vol. 28, nr 2, artikkel-id e1991
Emneord [en]
cloud computing, machine learning, quality of service, real-time network analytics, statistical learning, Forecasting, Learning systems, Regression analysis, Statistics, Testbeds, Video streaming, Design and implementations, Mean absolute error, Network statistics, Performance metrics, Prediction accuracy, Real time network, Real-time analytics, Distributed computer systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-33516DOI: 10.1002/nem.1991Scopus ID: 2-s2.0-85029351383OAI: oai:DiVA.org:ri-33516DiVA, id: diva2:1192827
Merknad

Funding details: VINNOVA; Funding details: 2013-03895, VINNOVA; This research has been supported by the Swedish Governmental Agency for Innovation Systems, VINNOVA, under grant 2013-03895.

Tilgjengelig fra: 2018-03-23 Laget: 2018-03-23 Sist oppdatert: 2018-08-17bibliografisk kontrollert

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