Methods to enhance content-distribution for very large scale online communities

1 June, 2016


The surge of the web 2.0 has encouraged an increase in the number of users interacting with systems through the Internet. These systems provide users with new capabilities in terms of sharing, adding and consuming information. This number of users and the amount of content they demand and consume grows in a steady manner. This fact, makes uncertain the future of these systems and brings the scientific community new challenges in terms of scalability and quality of service. Systems such as YouTube, Facebook, Twitter and Flicker among others, have demonstrated to offer a great variety of opportunities. Users contribute to the system by increasing the amount of available content and related information. This content is heterogeneous: videos, photos, news, music, comments, reviews, etc. There is a clear interaction between the users and the system and among the users themselves. Recent studies address the importance of understading these forms of interaction. All these studies target to create a solid theory explaining users interaction, and agree in the benefits of applying it to existing and future systems. Although there is a consensus about the benefits of applying social knowledge in order to improve performance, there is a gap between the theory and the definition of methods to exploit it. This gap is even bigger when we talk about how to apply these methods to real systems. The growing size of current systems brings the client/server paradigm to its limits, and even suggests the unfeasibility of continuing to use it as we know it in such a large systems. By contrast, paradigms such as P2P, have demonstrated to be extremely efficient in large distributed applications. Using this technology could suppose a clear improvement of the quality of service in the long term. To the best of our knowledge, the idea of combining P2P technology exploiting the existing social knowledge of these systems have not been deeply studied. This Ph.D. proposes the study and design of methods to enhance massive and heterogeneous content-distribution systems supporting very large scale online communities. These methods are intended to improve their global performance, and help to define a more solid theory about the explotation of social knowledge. The proposed methods include aspects such as system organization, community discovery or system evolution prediction.


author={Juan Manuel Tirado Martín},
title={Methods to enhance content-distribution for very large scale online communities},
school={Universidad Carlos III de Madrid}