ABSTRACT
IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service.
In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.
- D. Agarwal, D. Barman, D. Gunopulos, N. E. Young, F. Korn, and D. Srivastava. Efficient and effective explanation of change in hierarchical summaries. In ACM KDD, 2007. Google ScholarDigital Library
- B. Aggarwal, R. Bhagwan, V. N. Padmanabhan, and G. Voelker. NetPrints: Diagnosing home network misconfigurations using shared knowledge. In NSDI, 2009. Google ScholarDigital Library
- A. Arnold, Y. Liu, and N. Abe. Temporal causal modeling with graphical granger methods. In ACM KDD, pages 66--75, 2007. Google ScholarDigital Library
- P. Bahl, R. Chandra, A. Greenberg, S. Kandula, D. A. Maltz, and M. Zhang. Towards highly reliable enterprise network services via inference of multi-level dependencies. In Sigcomm, 2007. Google ScholarDigital Library
- W. Buntine. Theory refinement on Bayesian networks. In Proc. Uncertainty in artificial intelligence, 1991. Google ScholarDigital Library
- M. Cha, P. Rodriguez, J. Crowcroft, S. Moon, and X. Amatriain. Watching Television over an IP Network. In ACM IMC, 2008. Google ScholarDigital Library
- X. Chen, M. Zhang, Z. M. Mao, and P. Bahl. Automating network application dependency discovery: Experiences, limitations, and new solutions. In OSDI, 2008. Google ScholarDigital Library
- B. Cheng, L. Stein, H. Jin, and Z. Zhang. Towards cinematic internet video-on-demand. In ACM EuroSys, 2008. Google ScholarDigital Library
- P. R. Cohen, L. A. Ballesteros, D. E. Gregory, and R. S. Amant. Regression can build predictive causal models. Technical Report UM-CS-1994-015, 1994. Google ScholarDigital Library
- P. R. Cohen, D. E. Gregory, L. Ballesteros, and R. S. Amant. Two algorithms for inducing structural equation models from data. Technical Report UM-CS-1994-080, 1994. Google ScholarDigital Library
- G. F. Cooper and E. Herskovits. A bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309--347, 1992. Google ScholarCross Ref
- G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Finding hierarchical heavy hitters in data streams. In VLDB, 2003. Google ScholarDigital Library
- G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Diamond in the rough: finding hierarchical heavy hitters in multi-dimensional data. In ACM Sigmod, 2004. Google ScholarDigital Library
- A. Dhamdhere, R. Teixeira, C. Dovrolis, and C. Diot. Netdiagnoser: troubleshooting network unreachabilities using end-to-end probes and routing data. In ACM CoNEXT, 2007. Google ScholarDigital Library
- D.L.Donoho. For most large underdetermined systems of equations, the minimal l1-norm near solution approximates the sparsest near--solution. In http://www-stat.stanford.edu/ donoho/Reports/, 2004.Google Scholar
- C. Estan, S. Savage, and G. Varghese. Automatically inferring patterns of resource consumption in network traffic. In ACM Sigcomm, 2003. Google ScholarDigital Library
- C. W. J. Granger. Investigating causal relations by econometric models and cross-spectral methods. In Econometrica, 1969.Google ScholarCross Ref
- X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross. A measurement study of a large-scale P2P IPTV system. IEEE Transaction on Multimedia, 2007. Google ScholarDigital Library
- Y. Huang, T. Z. Fu, D.-M. Chiu, J. C. Lui, and C. Huang. Challenges, design and analysis of a large-scale P2P-VoD system. In ACM Sigcomm, 2008. Google ScholarDigital Library
- S. Kandula, R. Chandra, and D. Katabi. What's Going On? Learning Communication Rules in Edge Networks. In Sigcomm, 2008. Google ScholarDigital Library
- S. Kandula, D. Katabi, and J.-P. Vasseur. Shrink: A Tool for Failure Diagnosis in IP Networks. In MineNet, 2005. Google ScholarDigital Library
- R. R. Kompella, J. Yates, A. Greenberg, and A. C. Snoeren. Detection and localization of network blackholes. In Infocom, 2007.Google ScholarDigital Library
- A. Mahimkar, J. Yates, Y. Zhang, A. Shaikh, J. Wang, Z. Ge, and C. T. Ee. Troubleshooting chronic conditions in large IP networks. In ACM CoNEXT, 2008. Google ScholarDigital Library
- T. Qiu, Z. Ge, S. Lee, J. Wang, J. Xu, and Q. Zhao. Modeling channel popularity dynamics in a large IPTV system. In ACM Sigmetrics, 2009. Google ScholarDigital Library
- T. Silverston and O. Fourmaux. P2P IPTV measurement: a case study of TVants. In ACM CoNEXT, 2006. Google ScholarDigital Library
- P. Spirtes, C. N. Glymour, and R. Scheines. Causation, prediction and search. Lecture Notes in Statistics, 1993.Google Scholar
- K. Sridhar, G. Damm, and H. C. Cankaya. End-to-end diagnostics in IPTV architectures. Bell Lab. Tech. J., 2008. Google ScholarDigital Library
- M. Tariq, A. Zeitoun, V. Valancius, N. Feamster, and M. Ammar. Answering what-if deployment and configuration questions with WISE. In ACM SIGCOMM, 2008. Google ScholarDigital Library
- Wikipedia. Chebyshev inequality. http://en.wikipedia.org/wiki/Chebyshev's_inequality.Google Scholar
- S. A. Yemini, S. Kliger, E. Mozes, Y. Yemini, and D. Ohsie. High speed and robust event correlation. In IEEE Comm., 1996.Google ScholarDigital Library
- H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. Understanding user behavior in large-scale video-on-demand systems. ACM Sigops Operating Systems Review, 2006. Google ScholarDigital Library
- Y. Zhang, S. Singh, S. Sen, N. Duffield, and C. Lund. Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications. In ACM IMC, 2004. Google ScholarDigital Library
Index Terms
- Towards automated performance diagnosis in a large IPTV network
Recommendations
Towards automated performance diagnosis in a large IPTV network
SIGCOMM '09IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) ...
Modeling user activities in a large IPTV system
IMC '09: Proceedings of the 9th ACM SIGCOMM conference on Internet measurementInternet Protocol Television (IPTV) has emerged as a new delivery method for TV. In contrast with native broadcast in traditional cable and satellite TV system, video streams in IPTV are encoded in IP packets and distributed using IP unicast and ...
Mobile IPTV: Approaches, Challenges, Standards, and QoS Support
IPTV is defined as multimedia services, such as TV, video, audio, text, graphics, and data, delivered over IP-based networks managed to support quality of service (QoS), quality of experience, security, interactivity, and reliability. Mobile IPTV ...
Comments