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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | de Frein, R. |
| Copyright Year | 2015 |
| Description | Author affiliation: KTH R. Inst. of Technol., Stockholm, Sweden (de Frein, R.) |
| Abstract | An algorithm for predicting the quality of video received by a client from a shared server is presented. A statistical model for this client-server system, in the presence of other clients, is proposed. Our contribution is that we explicitly account for the interfering clients, namely the load. Once the load on the system is understood, accurate client-server predictions are possible with an accuracy of 12.4% load adjusted normalized mean absolute error. We continue by showing that performance measurement is a challenging sub-problem in this scenario. Using the correct measure of prediction performance is crucial. Performance measurement is miss-leading, leading to potential over-confidence in the results, if the effect of the load is ignored. We show that previous predictors have over (and under) estimated the quality of their prediction performance by up to 50% in some cases, due to the use of an inappropriate measure. These predictors are not performing as well as stated for about 60% of the service levels predicted. In summary we achieve predictions which are ≈50% more accurate than previous work using just ≈2% of the data to achieve this performance gain -a significant reduction in computational complexity results. |
| Starting Page | 1886 |
| Ending Page | 1894 |
| File Size | 459576 |
| Page Count | 9 |
| File Format | |
| e-ISBN | 9781509001545 |
| DOI | 10.1109/CIT/IUCC/DASC/PICOM.2015.280 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-26 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Analytical models Predictive models Prediction algorithms Data models Servers Load modeling |
| Content Type | Text |
| Resource Type | Article |
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