With the increased number of Software-Defined Networking (SDN) installations, the data centers of large service providers are becoming more and more agile in terms of network performance efficiency and flexibility. While SDN is an active and obvious trend in a modern data center design, the implications and possibilities it carries for effective and efficient network management are not yet fully explored and utilized. With most of the modern Internet traffic consisting of multimedia services and media-rich content sharing, the quality of multimedia communications is at the center of attention of many companies and research groups. Since SDN-enabled switches have an inherent feature of monitoring the flow statistics in terms of packets and bytes transmitted/lost, these devices can be utilized to monitor the essential statistics of the multimedia communications, allowing the provider to act in case of network failing to deliver the required service quality. The internal packet processing in the SDN switch enables the SDN controller to fetch the statistical information of the particular packet flow using the PacketIn and Multipart messages. This information, if preprocessed properly, can be used to estimate higher layer interpretation of the link quality and thus allowing to relate the provided quality of service (QoS) to the quality of user experience (QoE). This article discusses the experimental setup that can be used to estimate the quality of speech communication based on the information provided by the SDN controller. To achieve higher accuracy of the result, latency characteristics are added based on the exploiting of the dummy packet injection into the packet stream and/or RTCP packet analysis. The results of the experiment show that this innovative approach calculates the statistics of each individual RTP stream, and thus, we obtain a method for dynamic measurement of speech quality, where when quality decreases, it is possible to respond quickly by changing routing at the network level for each individual call. To improve the quality of call measurements, a Convolutional Neural Network (CNN) was also implemented. This model is based on two standard approaches to measuring the speech quality: PESQ and E-model. However, unlike PESQ/POLQA, the CNN-based model can take delay into account, and unlike the E-model, the resulting accuracy is much higher.

CESNET z s p o Zikova 4 160 00 Prague Czech Republic

Deparment of Telecommunications Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17 listopadu 2172 15 708 00 Ostrava Czech Republic

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Haleplidis E., Joachimpillai D., Salim J.H., Lopez D., Martin J., Pentikousis K., Denazis S., Koufopavlou O. ForCES ap-plicability to SDN-enhanced NFV; Proceedings of the Information Technology and Systems Conference; Bandung, Indonesia. 16–17 November 2015; pp. 951–956.

Caesar M., Caldwell D., Feamster N., Rexford J., Shaikh A., Merwe J. Design and Implementation of a Routing Control Platform; Proceedings of the 2nd Symposium on Networked Systems Design & Implementation; Boston, MA, USA. 2–4 May 2005; pp. 15–28.

OpenFlow Switch Specification—Version 1.5.1, Open Networking Foundation. [(accessed on 13 April 2021)]; Available online: https://www.opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf.

Frnda J., Voznak M., Rozhon J., Mehic M. Prediction model of QoS for Triple play services; Proceedings of the 2013 21st Telecommunications Forum Telfor; Belgrade, Serbia. 26–28 November 2013; pp. 733–736.

Methods for Subjective Determination of Transmission Quality, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/T-REC-P.800-199608-I/en.

Perceptual Evaluation of Speech Quality (PESQ): An Objective Method for End-to-End Speech Quality Assessment of Narrow-Band Telephone Networks and Speech Codecs, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/t-rec-p.862.

Rozhon J., Voznak M. Development of a speech quality monitoring tool based on ITU-T P.862; Proceedings of the 2011 34th International Conference on Telecommunications and Signal Processing (TSP); Budapest, Hungary. 18–20 August 2011; pp. 62–66.

De Rango F., Tropea M., Fazio P., Marano S. Overview on VoIP: Subjective and objective measurement methods. Int. J. Comput. Sci. Netw. Secur. 2006;6:140–153.

Perceptual Objective Listening Quality Prediction, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/t-rec-p.863.

Voznak M. Non-intrusive speech quality assessment in simplified E-model. WSEAS Trans. Syst. 2021;11:315–325.

The E-Model: A Computational Model for Use in Transmission Planning, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/T-REC-G.107-201506-I/en.

Transmission Impairments Due to Speech Processing, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/T-REC-G.113-200711-I/en.

De Rango F., Fazio P., Scarcello F., Conte F. A New Distributed Application and Network Layer Protocol for VoIP in Mobile Ad Hoc Networks. IEEE Trans. Mob. Comput. 2014;13:2185–2198. doi: 10.1109/TMC.2014.2307315. DOI

Voznak M., Rozhon J. Methodology for SIP infrastructure performance testing. WSEAS Trans. Comput. 2021;9:1012–1021.

Voznak M., Tomes M., Vaclavikova Z., Halas M. E-model improvement for speech quality evaluation including codecs tandeming; Proceedings of the International Conference on Data Networks, Communications, Computers; Faro, Portugal. 3–5 November 2010; pp. 119–124.

Rozhon J., Voznak M. SIP registration burst load test; Proceedings of the Communications in Computer and Information Science, 189 CCIS; Ostrava, Czech Republic. 7–9 July 2011; pp. 329–336.

Voznak M., Rozhon J. Approach to stress tests in SIP environment based on marginal analysis. Telecommun. Syst. 2011;52:1583–1593. doi: 10.1007/s11235-011-9525-1. DOI

Queiroz W., Capretz M.A., Dantas M. An approach for SDN traffic monitoring based on big data techniques. J. Netw. Comput. Appl. 2019;131:28–39. doi: 10.1016/j.jnca.2019.01.016. DOI

Vieira D., Juca P., Callado A. A Solution for QoS Provisioning in VoIP Services on the OpenFlow Platform; Proceedings of the Euro American Conference on Telematics and Information Systems (EATIS); Fortaleza, Brazil. 12–15 November 2018; pp. 1–5.

Rozhon J., Rezac F., Safarik J., Gresak E., Jalowiczor J. Measuring and monitoring the QoS and QoE in software defined networking environments; Proceedings of the Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; Baltimore, MD, USA. 15–17 April 2019.

Van Adrichem N.L.M., Doerr C., Kuipers F.A. OpenNetMon: Network monitoring in OpenFlow Software-Defined Networks; Proceedings of the 2014 IEEE Network Operations and Management Symposium (NOMS); Krakow, Poland. 5–9 May 2014; pp. 1–8.

Pulse Code Modulation (PCM) of Voice Frequencies, ITU-T. [(accessed on 13 April 2021)]; Available online: https://www.itu.int/rec/T-REC-G.711-198811-I/en.

Clark A.D. Modeling the effects of burst packet loss and recency on subjective voice quality; Proceedings of the IP Telephony Workshop; New York, NY, USA. 2–3 April 2001.

Hasslinger G., Hohlfeld O. The Gilbert-Elliott model for packet loss in real time services on the internet; Proceedings of the 14th GI/ITG Conference—Measurement, Modelling and Evalutation of Computer and Communication Systems; Dortmund, Germany. 31 March–2 April 2008; pp. 1–15.

Tropea M., Fedele G. Classifiers Comparison for Convolutional Neural Networks (CNNs) in Image Classification; Proceedings of the 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT); Cosenza, Italy. 7–9 October 2019; pp. 1–4.

Keles H.Y., Rozhon J., Ilk H.G., Voznak M. DeepVoCoder: A CNN Model for Compression and Coding of Narrow Band Speech. IEEE Access. 2019;7:75081–75089. doi: 10.1109/ACCESS.2019.2920663. DOI

Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going Deeper with Convolutions; Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Boston, MA, USA. 7–12 June 2015; pp. 1–9.