Abstract
Streaming video consumption has risen sharply over the last years. It has not only reshaped the Internet traffic, it has also changed the manner of watching videos. Users are progressively moving from the old-fashioned scheduled television to video-on-demand (VoD) services. As broadcasting future seems to be online, customers have become more sensitive to VoD quality, expecting ever-higher bitrates and lower rebuffering. In this context, average bitrate is a key quality of service (QoS) metric. Therefore, content delivery networks (CDNs) and content providers must be committed to enforce average bitrate through service-level agreement (SLA) contracts. Adaptive content replication is a promising technique towards this goal. However, this still offers a major challenge for CDN providers, particularly as they aim to avoid waste of resources. In this work, we introduce WiseReplica, an adaptive replication scheme for peer-assisted VoD systems that enforces the average bitrate for Internet videos. Using an accurate machine-learned ranking, WiseReplica saves storage and bandwidth from the vast majority of non-popular contents for the most watched videos. Simulations using YouTube traces suggest that our approach meets users expectations efficiently. Compared to caching, WiseReplica reduces the required replication degree for the most-watched videos by two orders of magnitude, and under heavy load, it increases the average bitrate by roughly 85 %.
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- 1.
Cisco Visual Networking Index: Forecast and Methodology, 2013–2018. www.cisco. com, 2014.
- 2.
Akamai acquires Red Swoosh. http://www.akamai.com/html/about/press/releases/2007/press_041207.html, April 2007.
- 3.
The Tube over Time: Characterizing Popularity Growth of YouTube Videos. http://www.vod.dcc.ufmg.br/traces/youtime/data/, January 2013.
- 4.
Advanced encoding settings for YouTube videos. http://support.google.com/youtube/bin/answer.py?hl=en-GB&answer=1722171, June 2014.
References
Adhikari, V.K., Jain, S., Chen, Y., Zhang, Z.-L.: Vivisecting youtube: an active measurement study. In: INFOCOM (2012)
Alizadeh, M., Greenberg, A., Maltz, D.A., Padhye, J., Patel, P., Prabhakar, B., Sengupta, S., Sridharan, M.: Data center TCP (DCTCP). In: SIGCOMM (2010)
Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I., Zhang, H.: Developing a predictive model of quality of experience for internet video. In: SIGCOMM (2013)
Bonvin, N., Papaioannou, T.G., Aberer, K.: A self-organized, fault-tolerant and scalable replication scheme for cloud storage. In: SOCC (2010)
Braun, L., Klein, A., Carle, G., Reiser, H., Eisl, J.: Analyzing caching benefits for youtube traffic in edge networks - a measurement-based evaluation. In: NOMS (2012)
Brodersen, A., Scellato, S., Wattenhofer, M.: Youtube around the world: geographic popularity of videos. In: WWW (2012)
Buffoni, D., Calauzenes, C., Gallinari, P., Usunier, N.: Learning scoring functions with order-preserving losses and standardized supervision. In: ICML (2011)
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning (2005)
Calauzenes, C., Usunier, N., Gallinari, P., et al.: On the (non-) existence of convex, calibrated surrogate losses for ranking. In: Neural Information Processing Systems (2012)
Chang, L., Pan, J.: Towards the optimal caching strategies of peer-assisted VoD systems with HD channels. In: ICNP (2012)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley, Reading (2010)
Dobrian, F., Sekar, V., Awan, A., Stoica, I., Joseph, D., Ganjam, A., Zhan, J., Zhang, H.: Understanding the impact of video quality on user engagement. In: SIGCOMM (2011)
Figueiredo, F., Benevenuto, F., Almeida, J.M.: The tube over time: characterizing popularity growth of youtube videos. In: WSDM (2011)
Finamore, A., Mellia, M., Munafò, M.M., Torres, R., Rao, S.G.: Youtube everywhere: impact of device and infrastructure synergies on user experience. In: IMC (2011)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction: With 200 Full-Color Illustrations. Springer, New York (2001)
Huang, C., Li, J., Ross, K.W.: Can internet video-on-demand be profitable? In: SIGCOMM (2007)
Huang, Y., Fu, T.Z., Chiu, D.-M., Lui, J.C., Huang, C.: Challenges, design and analysis of a large-scale P2P VoD system. In: Sigcomm (2008)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM TOIS 20, 422–446 (2002)
Jin, S., Bestavros, A.: Popularity-aware greedy-dual-size web proxy caching algorithms. In: ICDCS (1999)
Liu, X., Dobrian, F., Milner, H., Jiang, J., Sekar, V., Stoica, I., Zhang, H.: A case for a coordinated internet video control plane. In: SIGCOMM (2012)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Mansy, A., Ammar, M.H.: Analysis of adaptive streaming for hybrid CDN/P2P live video systems. In: ICNP (2011)
Montresor, A., Jelasity, M.: PeerSim: a scalable P2P simulator. In: P2P (2009)
Parvez, N., Williamson, C., Mahanti, A., Carlsson, N.: Analysis of bittorrent-like protocols for on-demand stored media streaming. In: SIGMETRICS (2008)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011)
Shen, H.: An efficient and adaptive decentralized file replication algorithm in P2P file sharing systems. IEEE Trans. Parallel Distrib. Syst. 21, 827–840 (2010)
Silvestre, G., Fernandes, S., Kamienski, C., Sadok, D.: Most wanted internet applications: a framework for P2P identification. In: CNSR (2010)
Silvestre, G., Monnet, S., Krishnaswamy, R., Sens, P.: Aren: a popularity aware replication scheme for cloud storage. In: ICPADS (2012)
Silvestre, G., Monnet, S., Krishnaswamy, R., Sens, P.: Caju: a content distribution system for edge networks. Technical report, UPMC Sorbone Universités (2012)
Steinwart, I.: How to Compare different loss functions and their risks. Constr. Approx. 26, 225–287 (2007)
Bittorrent. http://bittorrent.com
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53, 80–88 (2010)
Wilson, C., Ballani, H., Karagiannis, T., Rowstron, A.: Better never than late: meeting deadlines in datacenter networks. In: SIGCOMM (2011)
Zhang, T.: Statistical behavior and consistency of classification methods based on convex risk minimization. Ann. Stat. 32(1), 56–134 (2004)
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Silvestre, G., Buffoni, D., Pires, K., Monnet, S., Sens, P. (2015). Boosting Streaming Video Delivery with WiseReplica. In: Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Wang, L., Zomaya, A. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XX. Lecture Notes in Computer Science(), vol 9070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46703-9_2
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