Quality of Experience (QoE) metrics describe the service usability from the endusers’ point of view. In a networking environment QoE metrics are very close to Quality of Service (QoS) metrics, except the fact that end-user experience is subjective in nature, moreover, it is also influenced by the access capabilities of end users and the used service path. Our ultimate aim is to find methods determining QoE by passive measurements on an aggregated network link. One step towards this is determining the correlation between network overload and QoE. There can be several scenarios where the experienced service quality becomes less than satisfactory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aad van Moorsel (2001) Metrics for the Internet Age: Quality of Experi-ence and Quality of Business. E-Services Software Research Department, Hewlett-Packard Laboratories, Palo Alto, California, USA.
Pál Varga, Gergely Kún, Péter Fodor, József Bíró, Daisuke Satoh, Kei-suke Ishibashi (2003) An Advanced Technique on Bottleneck Detection. Proc IFIP WG6.3 workshop, EUNICE 2003, pp 52-56, Balatonfüred, Hungary.
Yoshihiro Ito, Shuji Tasaka, Yoshihiko Fukuta (2004) Psychometric Analysis of the Effect of End-to-End Delay on User-Level QoS in Live Audio-Video Transmission. Proc the ACM Workshop on Next-Generation Residential Broadband Challenges (NRBC'04), pp 2-10, New york, NY, USA
Pál Varga (2005) Analyzing Packet Interarrival Times Distribution to Detect Network Bottlenecks. Proc IFIP WG6.3 workshop, EUNICE 2005, Balatonfüred, Hungary.
Dina Katabi, Charles Blake (2002) Inferring congestion sharing and path characteristics from packet interarrival times. In Technical Report MIT-LCS-TR-828, MIT.
Pál Varga, Gergely Kún (2005) Utilizing Higher Order Statistics of Packet Interarrival Times for Bottleneck Detection. In IFIP/IEEE E2EMON’05, Nice, France.
L. Kleinrock, (1975) Queuing systems, volume 1: Theory. John Wiley and Sons, Inc., ISBN 963 10 2725 2.
Gergely Kún, Pál Varga (2006) Utilizing MGR-PS model properties for bottleneck characterization, Proc WTC2006, Budapest, Hungary.
Francesco Vacirca, Thomas Ziegler, Eduard Hasenleithner (2006) An Algorithm to Detect TCP Spurious Timeouts and its Application to Op-erational UMTS/GPRS Networks. Journal of Computer Networks, Elsevier.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this paper
Cite this paper
Varga, P., Kún, G., Sey, G. (2007). Towards Estimating Quality of Experience with Passive Bottleneck Detection Metrics. In: Wojtkowski, W., Wojtkowski, W.G., Zupancic, J., Magyar, G., Knapp, G. (eds) Advances in Information Systems Development. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-70802-7_10
Download citation
DOI: https://doi.org/10.1007/978-0-387-70802-7_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-70801-0
Online ISBN: 978-0-387-70802-7
eBook Packages: Computer ScienceComputer Science (R0)