Framework for Internet Data Quality Measurement in Telecommunication Networks
Keywords:
Telecommunication NetworkAbstract
This paper explores the quality of Internet download performance by examining the most important network performance measures and designing a complex measuring framework of user-level service quality. There are a number of technical parameters such as throughput, latency, jitter and reliability of data transmission that affect internet download experience. Yet existing network monitoring systems utilized by operators are mostly concerned with the infrastructure level measurements and do not necessarily reflect the quality as perceived by the end users. Thus, this study suggests a composite measurement scale called Internet Download Quality Score (IDQS) that combines various network performance measures into one measure that can be explained to measure the quality of downloads at the user session level.
The research paper follows the quantitative research paradigm based on session-based download measurements made by several users under various network conditions. The most important variables were downloading speed, latency, jitter, packet loss, signal strength and unsuccessful downloads which were analyzed in MATLAB to determine statistical correlations between network parameters and perceived download quality. The impact of these indicators on the performance of download was assessed using correlation analysis, as well as descriptive statistics and predictive modelling techniques.
The findings reveal that the parameters of reliability like download failure and packet loss can have a great effect on the user experience in contrast to the moderate changes in throughput. The presented IDQS model manages to merge the speed and reliability measures to categorize downloading sessions into the acceptable and poor quality. The model was also found to have high predictive accuracy and it also gives a simplified measure of quality which can be used by network operators to detect service degradation and to give priority to optimization activities and enhance user satisfaction.
In general, the results can be used to create a user-centric approach to Internet performance assessment which can fill the void between the classic metrics of network performance and user experience.
References
1. Akella, A., Seshan, S., & Shaikh, A. (2003). An empirical evaluation of wide-area Internet bottlenecks. *Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement*, 85–98. [https://doi.org/10.1145/948205.948216](https://doi.org/10.1145/948205.948216)
2. Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I., & Zhang, H. (2012). A quest for an Internet video quality-of-experience metric. *Proceedings of the 11th ACM Workshop on Hot Topics in Networks*, 97–102. [https://doi.org/10.1145/2390231.2390245](https://doi.org/10.1145/2390231.2390245)
3. Bansal, S., & Balakrishnan, H. (2001). TCP improvements for heterogeneous networks. *ACM SIGCOMM Computer Communication Review, 31*(4), 277–288.
4. Cisco. (2023). *Cisco Annual Internet Report (2018–2023) White Paper*. Cisco Systems.
5. De Vriendt, J., De Vleeschauwer, D., & Robinson, D. (2001). Model for estimating end-to-end performance of IP services. *IEEE Communications Magazine, 39*(1), 76–83.
6. ETSI. (2019). *ETSI GS QOE 002: Quality of Experience (QoE); Requirements for QoE monitoring*. European Telecommunications Standards Institute.
7. Fiedler, M., Hossfeld, T., & Tran-Gia, P. (2010). A generic quantitative relationship between quality of experience and quality of service. *IEEE Network, 24*(2), 36–41.
8. García-Dorado, J. L., Finamore, A., Mellia, M., Munafò, M., & Rossi, D. (2012). Characterization of ISP traffic: Trends, user habits, and access technology impact. *IEEE Transactions on Network and Service Management, 9*(2), 142–155.
9. Hossfeld, T., Schatz, R., & Egger, S. (2011). SOS: The MOS is not enough! *Proceedings of the 3rd International Workshop on Quality of Multimedia Experience (QoMEX)*, 131–136.
10. ITU-T. (2016). *Recommendation P.10/G.100: Vocabulary for performance, quality of service and quality of experience*. International Telecommunication Union.
11. ITU-T. (2017). *Recommendation G.1010: End-user multimedia QoS categories*. International Telecommunication Union.
12. Jain, R. (1991). *The art of computer systems performance analysis*. Wiley.
13. Kleinrock, L. (1975). *Queueing systems, volume 1: Theory*. Wiley.
14. Kurose, J. F., & Ross, K. W. (2021). *Computer networking: A top-down approach* (8th ed.). Pearson.
15. Liu, J., & Shen, H. (2018). QoE-aware resource allocation for mobile networks. *IEEE Access, 6*, 26183–26194.
16. Machado, J., & Sampaio, P. (2013). Service quality measurement models: A review. *Total Quality Management & Business Excellence, 24*(5–6), 577–593.
17. Mellia, M., Munafò, M., Rossi, D., & Finamore, A. (2011). Revealing ISP traffic shaping. *ACM SIGCOMM Computer Communication Review, 40*(2), 13–24.
18. Mishra, A. (2004). *Fundamentals of cellular network planning and optimisation*. Wiley.
19. Padhye, J., Firoiu, V., Towsley, D., & Kurose, J. (1998). Modeling TCP throughput. *ACM SIGCOMM Computer Communication Review, 28*(4), 303–314.
20. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. *Journal of Retailing, 64*(1), 12–40.
21. Reichl, P., Schatz, R., & Egger, S. (2010). The logarithmic nature of QoE and QoS relationship. *Proceedings of the IEEE International Conference on Communications*, 1–5.
22. Rix, A., Hollier, M., Hekstra, A., & Beerends, J. (2001). Perceptual evaluation of speech quality (PESQ). *IEEE International Conference on Acoustics, Speech, and Signal Processing*, 749–752.
23. Sharma, V., & Mazumdar, R. (2004). Hybrid ARQ in wireless networks. *IEEE Transactions on Communications, 52*(7), 1200–1211.
24. Stevens, W. R. (1997). *TCP/IP illustrated, volume 1*. Addison-Wesley.
25. Tan, P. N., Steinbach, M., & Kumar, V. (2019). *Introduction to data mining* (2nd ed.). Pearson.
26. Tanenbaum, A. S., & Wetherall, D. J. (2021). *Computer networks* (6th ed.). Pearson.
27. Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. *IEEE Transactions on Image Processing, 13*(4), 600–612.
28. Zorzi, M., & Rao, R. (1997). Error control and retransmission strategies in wireless networks. *IEEE Communications Magazine, 35*(1), 52–60.
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