Evaluation of Machine Learning Methods for Predicting Client Metrics for a Telecom Service

A video streaming service faces several difficultiesoperating. Hardware is expensive and it is crucial to prioritizecustomers in a way that will make them content with the serviceprovided. That is, deliver a sufficient frame rate and neverallocate too much, essentially waste, resources on a client. Thisallocation has to be done several times per second so readingdata from the client is out of the question, because the systemwould be adapting too slow. This raises the question whether it ispossible to predict the frame rate of a client using only variablesmeasured on the server and if it can be done efficiently. Which itcan [1]. To further build on the work of Yanggratoke et al [1], weevaluated several different machine learning methods on a dataset in terms of performance, training time and dependence on thesize of the data set. Neural networks, having the best adaptingcapabilities, resulted in the best performance but training is moretime consuming than for the linear model. Using neural networksis a good idea when the relationship between input and outputis not linear