TY - GEN
T1 - On the Assessment and Debugging of QoE in SDN
T2 - 18th IEEE International Symposium on Network Computing and Applications, NCA 2019
AU - Reyes, Jose
AU - Lopez, Jorge
AU - Kushik, Natalia
AU - Zeghlache, Djamal
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - The paper presents a work in progress on the Quality of Experience (QoE) assessment in the context of Software Defined Networking (SDN). Preliminary experimental results clearly demonstrate the necessity of considering QoE parameters, which are not taken into account in traditional networks. Dynamic network (re-) configuration requires monitoring and measuring attributes not only at the network level but also at the control and application planes. We discuss a list of such parameters and propose a monitoring algorithm to consider distributed and asynchronous points of observation for the QoE assessment of SDN network services. Further, for the cases when a low QoE level is detected (or predicted using machine learning), we propose an algorithm for effective diagnosis of the QoE degradation, based on an iterative deduction of a QoE attribute contributing the most to a low QoE value.
AB - The paper presents a work in progress on the Quality of Experience (QoE) assessment in the context of Software Defined Networking (SDN). Preliminary experimental results clearly demonstrate the necessity of considering QoE parameters, which are not taken into account in traditional networks. Dynamic network (re-) configuration requires monitoring and measuring attributes not only at the network level but also at the control and application planes. We discuss a list of such parameters and propose a monitoring algorithm to consider distributed and asynchronous points of observation for the QoE assessment of SDN network services. Further, for the cases when a low QoE level is detected (or predicted using machine learning), we propose an algorithm for effective diagnosis of the QoE degradation, based on an iterative deduction of a QoE attribute contributing the most to a low QoE value.
KW - Debugging/Diagnosis
KW - Machine Learning
KW - Quality of Experience
KW - Software Defined Networking
UR - https://www.scopus.com/pages/publications/85077968125
U2 - 10.1109/NCA.2019.8935029
DO - 10.1109/NCA.2019.8935029
M3 - Conference contribution
AN - SCOPUS:85077968125
T3 - 2019 IEEE 18th International Symposium on Network Computing and Applications, NCA 2019
BT - 2019 IEEE 18th International Symposium on Network Computing and Applications, NCA 2019
A2 - Gkoulalas-Divanis, Aris
A2 - Marchetti, Mirco
A2 - Avresky, Dimiter R.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 September 2019 through 28 September 2019
ER -