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Predicting Distributions of Service Metrics using Neural Networks
KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.
2018 (engelsk)Inngår i: 2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) / [ed] Salsano, S Riggio, R Ahmed, T Samak, T DosSantos, CRP, IEEE , 2018, s. 45-53Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We predict the conditional distributions of service metrics, such as response time or frame rate, from infrastructure measurements in a cloud environment. From such distributions, key statistics of the service metrics, including mean, variance, or percentiles can be computed, which are essential for predicting SLA conformance or enabling service assurance. We model the distributions as Gaussian mixtures, whose parameters we predict using mixture density networks, a class of neural networks. We apply the method to a Voll service and a KY store running on our lab testbed. The results validate the effectiveness of the method when applied to operational data. In the case of predicting the mean of the frame rate or response time, the accuracy matches that of random forest, a baseline model.

sted, utgiver, år, opplag, sider
IEEE , 2018. s. 45-53
Serie
International Conference on Network and Service Management, ISSN 2165-9605
Emneord [en]
Service Engineering, Machine Learning, Generative Models, Network Management
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-245985ISI: 000458916300006Scopus ID: 2-s2.0-85060906697ISBN: 978-3-9031-7614-0 (tryckt)OAI: oai:DiVA.org:kth-245985DiVA, id: diva2:1295175
Konferanse
14th International Conference on Network and Service Management (CNSM), NOV 05-09, 2018, Rome, ITALY
Merknad

QC 20190311

Tilgjengelig fra: 2019-03-11 Laget: 2019-03-11 Sist oppdatert: 2019-08-20bibliografisk kontrollert

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Samani, Forough ShahabStadler, Rolf

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