Group 2
Members:
Bohlen, Kurtis (kbohlen@sfu.ca)
Jovasevic, Dejan (djovasev@sfu.ca)
Thomas, Rohan (rohant@sfu.ca)
In the United States and Canada alone, Netflix has north of 25 million users [1], accounting for over 30% of all downstream traffic in the US [2]. With so much traffic Netflix employs a system of servers that form a Content Distribution Network (CDN) from which the video “chunks” are cached and streamed to the user [3]. This is in an attempt to reduce the load on the network and improve the customer experience. With LTE providing throughput speeds similar to high speed internet access the demand for mobile live streamed HD video has increased [4]. We will investigate the performance of the LTE network for streaming Netflix movies to mobile devices and analyze how varying the proximity and design of Netflix’s CDN, called Open Connect, impacts this performance [5]. In our analysis we will look at the bit rates of the transfers, loss of packets, and delays. We will be using Riverbed Modeler to create testing scenarios, in which we will change the number and location of the CDNs video servers servicing both stationary and moving mobile devices.
More DetailsLong-Term Evolution (LTE) is the latest technology deployed in high speed cellular networks allowing for speeds similar to cable high speed internet access. Over 60 countries have adopted the standard globally and the total number continues to grow.
With over 90 million worldwide users, Netflix is the home of on-demand video streaming for multiple platforms both wired and mobile. 4K quality makes it the ultimate viewing experience on devices connected to high speed networks.
We live in a world where users interact with web content based on their location. This is all the beauty of Content Distribution Networks (CDN) - where networks are distributed to reduce bandwidth needs, improve response times, and deliver a high quality of experience.
Riverbed Modeler is a development environment containing numerous protocols and technologies - allowing users to model various network types and analyze their performance.
[1] V. K. Adhikari, Y. Guo, F. Hao, M. Varvello, V. Hilt, M. Steiner, and Z. Zhang, "Unreeling netflix: Understanding and improving multi-CDN movie delivery," in Proc. IEEE INFOCOM, Orlando, FL, 2012, pp. 1620-1628. doi: 10.1109/INFCOM.2012.6195531
[2] V. K. Adhikari, Y. Guo, F. Hao, V. Hilt, Z. Zhang, M. Varvello, and M. Steiner, "Measurement Study of Netflix, Hulu, and a Tale of Three CDNs," IEEE/ACM Transactions on Networking, vol. 23, no. 6, pp. 1984-1997, Dec. 2015. doi: 10.1109/TNET.2014.2354262
[3] J. Summers, T. Brecht, D. Eager, and A. Gutarin, "Characterizing the workload of a netflix streaming video server," in Proc. IEEE International Symposium on Workload Characterization (IISWC), Providence, RI, 2016, pp. 1-12. doi: 10.1109/IISWC.2016.7581265
[4] C.D. Cranor, M. Green, C. Kalmanek, D. Shur, S. Sibal, J.E. Van der Merwe, and C.J. Sreenan, "Enhanced streaming services in a content distribution network," IEEE Internet Computing, vol. 5, no. 4, pp. 66-75, Jul/Aug 2001. doi: 10.1109/4236.939452
[5] T.Böttger, F. Cuadrado, G. Tyson, I. Castro, and S. Uhlig, Open Connect Everywhere: A Glimpse at the Internet Ecosystem through the Lens of the Netflix CDN, eprint arXiv:1606.05519 [cs.NI], 2016, pp. 1-14.