Change search
Link to record
Permanent link

Direct link
BETA
Samani, Forough Shahab
Publications (2 of 2) Show all publications
Samani, F. S., Stadler, R., Johnsson, A. & Flinta, C. (2019). Demonstration: Predicting Distributions of Service Metrics. In: 2019 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM): . Paper presented at 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019; Arlington; United States; 8 April 2019 through 12 April 2019 (pp. 745-746). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8717915.
Open this publication in new window or tab >>Demonstration: Predicting Distributions of Service Metrics
2019 (English)In: 2019 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 745-746, article id 8717915Conference paper, Published paper (Refereed)
Abstract [en]

The ability to predict conditional distributions of service metrics is key to understanding end-to-end service behavior. From conditional distributions, other metrics can be derived, such as expected values and quantiles, which are essential for assessing SLA conformance. Our demonstrator predicts conditional distributions and derived metrics estimation in real-time, using infrastructure measurements. The distributions are modeled as Gaussian mixtures whose parameters are estimated using a mixture density network. The predictions are produced for a Video-on-Demand service that runs on a testbed at KTH.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Service Engineering, Service Management, Machine Learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-254135 (URN)000469937200144 ()978-3-903176-15-7 (ISBN)
Conference
2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019; Arlington; United States; 8 April 2019 through 12 April 2019
Note

QC 20190625

Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-06-25Bibliographically approved
Samani, F. S. & Stadler, R. (2018). Predicting Distributions of Service Metrics using Neural Networks. In: Salsano, S Riggio, R Ahmed, T Samak, T DosSantos, CRP (Ed.), 2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM): . Paper presented at 14th International Conference on Network and Service Management (CNSM), NOV 05-09, 2018, Rome, ITALY (pp. 45-53). IEEE
Open this publication in new window or tab >>Predicting Distributions of Service Metrics using Neural Networks
2018 (English)In: 2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) / [ed] Salsano, S Riggio, R Ahmed, T Samak, T DosSantos, CRP, IEEE , 2018, p. 45-53Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Network and Service Management, ISSN 2165-9605
Keywords
Service Engineering, Machine Learning, Generative Models, Network Management
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-245985 (URN)000458916300006 ()2-s2.0-85060906697 (Scopus ID)978-3-9031-7614-0 (ISBN)
Conference
14th International Conference on Network and Service Management (CNSM), NOV 05-09, 2018, Rome, ITALY
Note

QC 20190311

Available from: 2019-03-11 Created: 2019-03-11 Last updated: 2019-08-20Bibliographically approved
Organisations

Search in DiVA

Show all publications