Demonstration : Predicting distributions of service metrics

• 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 realtime, 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Machine Learning

Service Engineering

Service Management

Forecasting

Learning systems

Video on demand

Conditional distribution

End-to-end service

Expected values

Gaussian mixtures

Mixture density

Video on demand services

Telecommunication services

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)