skip to main content
10.1145/2934872.2934907acmconferencesArticle/Chapter ViewAbstractPublication PagescommConference Proceedingsconference-collections
research-article
Free Access

Via: Improving Internet Telephony Call Quality Using Predictive Relay Selection

Published:22 August 2016Publication History

ABSTRACT

Interactive real-time streaming applications such as audio-video conferencing, online gaming and app streaming, place stringent requirements on the network in terms of delay, jitter, and packet loss. Many of these applications inherently involve client-to-client communication, which is particularly challenging since the performance requirements need to be met while traversing the public wide-area network (WAN). This is different from the typical situation of cloud-to-client communication, where the WAN can often be bypassed by moving a communication end-point to a cloud “edge”, close to the client. Can we nevertheless take advantage of cloud resources to improve the performance of real-time client-to-client streaming over the WAN?

In this paper, we start by analyzing data from a large VoIP provider whose clients are spread across over 21,000 AS’es and nearly all the countries, to understand the challenges faced by interactive audio streaming in the wild. We find that while inter-AS and international paths exhibit significantly worse performance than intra-AS and domestic paths, the pattern of poor performance is nevertheless quite scattered, both temporally and spatially. So any effort to improve performance would have to be fine-grained and dynamic.

Then, we turn to the idea of overlay routing, but in the context of the well-provisioned, managed network of a cloud provider rather than peer-to-peer as has been considered in past work. Such a network typically has a global footprint and peers with a large number of network providers. When the performance of a call via the direct path is predicted to be poor, the call traffic could be directed to enter the managed network close to one end point and exit it close to the other end point, thereby avoiding wide-area communication over the public Internet. We present and evaluate data-driven techniques to deciding whether to relay a call through the managed network and if so how to pick the ingress and egress relays to maximize performance, all while operating within a budget for relaying calls via the managed overlay network. We show that call performance can potentially improve by 40%-80% on average, with our techniques closely matching it.

Skip Supplemental Material Section

Supplemental Material

p286.mp4

mp4

257.9 MB

References

  1. 1.G.107: The E-Model, a computational model for use in transmission planning. https://www.itu.int/rec/T-REC-G.107-201506-I/en.Google ScholarGoogle Scholar
  2. 2.G.114: ITU Recommendation of One-way Transmission Time. https://www.itu.int/rec/T-REC-G.114/en.Google ScholarGoogle Scholar
  3. 3.Microsoft: Skype runs on Windows Azure; SkyDrive up next. http://www.zdnet.com/article/ microsoft-skype-runs-on-windows-azure-skydrive-up-next/.Google ScholarGoogle Scholar
  4. 4.Quality of Service for Voice over IP. http://www.cisco.com/c/en/us/td/docs/ios/solutions_docs/ qos_solutions/QoSVoIP/QoSVoIP.pdf.Google ScholarGoogle Scholar
  5. 5.Skype’s Incredible Rise, in One Image. http://blogs.wsj.com/ digits/2014/01/15/skypes-incredible-rise-in-one-image/.Google ScholarGoogle Scholar
  6. 6.WhatsApp Calling: 100 million conversations every day. https://blog.whatsapp.com/10000625/ WhatsApp-Calling-100-million-conversations-every-day, Jun 23, 2016.Google ScholarGoogle Scholar
  7. 7.S. Agarwal and J. R. Lorch. Matchmaking for Online Games and Other Latency-sensitive P2P Systems. In In SIGCOMM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.D. G. Andersen, H. Balakrishnan, M. F. Kaashoek, and R. Morris. Resilient Overlay Networks. In SOSP, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.S. Banerji, B. Bhattacharjee, and C. Kommareddy. Scalable Application Layer Multicast. In SIGCOMM, 2002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.W. Cao, J. Li, Y. Tao, and Z. Li. On top-k selection in multi-armed bandits and hidden bipartite graphs. In Advances in Neural Information Processing Systems, pages 1036–1044, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.R. Castro, M. Coates, G. Liang, R. Nowak, and B. Yu. Network Tomography: Recent Developments. Statistical Science, 19(3):499–517, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  13. 13.C.-N. Chen, C.-Y. Chu, S.-L. Yeh, H. hua Chu, and P. Huang. Measuring the Perceptual Quality of Skype Sources. In ACM SIGCOMM Workshop on Measurements up the Stack (W-MUST), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.C.-N. Chen, C.-Y. Chu, S.-L. Yeh, H. hua Chu, and P. Huang. Modeling the QoE of Rate Changes in SKYPE/SILK VoIP Calls. In ACM Multimedia, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.K.-T. Chen, C.-Y. Huang, P. Huang, and C.-L. Lei. Quantifying Skype User Satisfaction. In SIGCOMM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.M. Chesire, A. Wolman, G. M. Voelker, and H. M. Levy. Measurement and Analysis of a Streaming Media Workload. In Usenix USITS, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.R. G. Cole and J. H. Rosenbluth. Voice over IP Performance Monitoring. ACM SIGCOMM Computer Communication Review, 31(2):9–24, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18.F. Dabek, R. Cox, F. Kaashoek, and R. Morris. Vivaldi: A Decentralized Network Coordinate System. In SIGCOMM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.F. Dobrian, V. Sekar, A. Awan, I. Stoica, D. Joseph, A. Ganjam, J. Zhan, and H. Zhang. Understanding the Impact of Video Quality on User Engagement. In SIGCOMM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.F. Dobrian, V. Sekar, A. Awan, I. Stoica, D. A. Joseph, A. Ganjam, J. Zhan, and H. Zhang. Understanding the impact of video quality on user engagement. In Proc. SIGCOMM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21.H. Eriksson. MBone: The Multicast Backbone. Communications of the ACM (CACM), Aug. 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.P. Francis, S. Jamin, C. Jin, Y. Jin, D. Raz, Y. Shavitt, and L. Zhang. IDMaps: A Global Internet Host Distance Estimation Service. IEEE/ACM Trans. Netw., 9(5):525–540, Oct. 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. 23.R. Frederick, V. Jacobson, and P. Design. RTP: A Transport Protocol for Real-time Applications. IETF RFC3550, 2003.Google ScholarGoogle Scholar
  24. 24.A. Ganjam, F. Siddiqi, J. Zhan, I. Stoica, J. Jiang, V. Sekar, and H. Zhang. C3: Internet-scale control plane for video quality optimization. In NSDI. USENIX, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.O. Haq and F. R. Dogar. Leveraging the Power of Cloud for Reliable Wide Area Communication. In ACM Workshop on Hot Topics in Networks, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26.R. Kateja, N. Baranasuriya, V. Navda, and V. N. Padmanabhan. DiversiFi: Robust Multi-Link Interactive Streaming. In ACM CoNext, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. 27.W. Kho, S. A. Baset, and H. Schulzrinne. Skype Relay Calls: Measurements and Experiments. In IEEE Infocom Global Internet Workshop, 2008.Google ScholarGoogle Scholar
  28. 28.H. V. Madhyastha, T. Isdal, M. Piatek, C. Dixon, T. Anderson, A. Krishnamurthy, and A. Venkataramani. iplane: An information plane for distributed services. In USENIX OSDI ’06. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. 29.T. E. Ng and H. Zhang. Predicting Internet Network Distance with Coordinates-based Approaches. In IEEE INFOCOM, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  30. 30.V. N. Padmanabhan and L. Qiu. The Content and Access Dynamics of a Busy Web Site: Findings and Implications. In SIGCOMM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. 31.D. Pendarakis, S. Shi, D. Verma, and M. Waldvogel. ALMI: An Application Level Multicast Infrastructure. In Usenix USITS, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. 32.S. Saroiu, K. P. Gummadi, R. J. Dunn, S. D. Gribble, and H. M. Levy. An Analysis of Internet Content Delivery Systems. In OSDI, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. 33.S. Savage, A. Collins, E. Hoffman, J. Snell, and T. Anderson. The End-to-end Effects of Internet Path Selection. In SIGCOMM, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. 34.R. Torres, A. Finamore, J. R. Kim, M. Mellia, M. M. Munafo, and S. Rao. Dissecting Video Server Selection Strategies in the YouTube CDN. In ICDCS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. 35.P. Wendell, J. W. Jiang, M. J. Freedman, and J. Rexford. DONAR: Decentralized Server Selection for Cloud Services. In SIGCOMM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. 36.H. Xie and Y. R. Yang. A Measurement-based Study of the Skype Peer-to-Peer VoIP Performance. In IPTPS, 2012.Google ScholarGoogle Scholar
  37. 37.Y. Xu, C. Yu, J. Li, and Y. Liu. Video Telephony for End-consumers: Measurement Study of Google+, iChat, and Skype. In IMC, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Via: Improving Internet Telephony Call Quality Using Predictive Relay Selection

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            SIGCOMM '16: Proceedings of the 2016 ACM SIGCOMM Conference
            August 2016
            645 pages
            ISBN:9781450341936
            DOI:10.1145/2934872

            Copyright © 2016 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 22 August 2016

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            SIGCOMM '16 Paper Acceptance Rate39of231submissions,17%Overall Acceptance Rate554of3,547submissions,16%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader