Video Popularity Metrics and Bubble Cache Eviction Algorithm Analysis

Video data is the largest type of traffic in the Internet, currently responsible for over 72% of the total traffic, with over 883PB of data per month in 2016. Large scale CDN solutions are available that offer a variety of distributed hosting platforms for the purpose of transmitting video over IP. However, the IP protocol, unlike ICN protocol implementations, does not provide an any-cast architecture from which a CDN would greatly benefit. In this thesis we introduce a novel cache eviction strategy called ``Bubble,'' as well as two variants of Bubble, that can be applied to any-cast protocols to aid in optimising video delivery. Bubble, Bubble-LRU and Bubble-Insert were found to greatly reduce the quantity of video associated traffic observed in cache enabled networks. Additionally, analysis on two British Telecom (BT) provided video popularity distributions leveraging Kullback-Leibler and Pearson Chi-Squared testing methods was performed. This was done to assess which model, Zipf or Zipf-Mandelbrot, is best suited to replicate video popularity distributions and the results of these tests conclude that Zipf-Mandelbrot is the most appropriate model to replicate video popularity distributions. The work concludes that the novel cache eviction algorithms introduced in this thesis provide an efficient caching mechanism for future content delivery networks and that the modelled Zipf-Mandelbrot distribution is a better method for simulating the performance of caching algorithms.