TY - GEN
T1 - Take off a load
T2 - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
AU - De Fréin, Ruairí
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84964239596&partnerID=8YFLogxK
U2 - 10.1109/CIT/IUCC/DASC/PICOM.2015.280
DO - 10.1109/CIT/IUCC/DASC/PICOM.2015.280
M3 - Conference contribution
AN - SCOPUS:84964239596
T3 - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
SP - 1886
EP - 1894
BT - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
A2 - Atzori, Luigi
A2 - Jin, Xiaolong
A2 - Jarvis, Stephen
A2 - Liu, Lei
A2 - Calvo, Ramon Aguero
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Wu, Yulei
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 October 2015 through 28 October 2015
ER -