Similarity based clustering in large-scale, decentralized overlay networks

Collaborative filtering, such as recommendation algorithms for example where Amazon may recommend books to a customer based on other customers’ feedback and profile similarity, and/or similarity between other books, has become a key approach for effectively providing people with the most relevant information to them. Some large-scale applications, like file sharing, instant news broadcasting, and personal data management, are decentralized in their operation, and the standard centralized approach for collaborative filtering, whereby a centralized system receives all of the data and computes the outcomes, is not applicable. For these applications to remain decentralized while making use of collaborative filtering, requires research into how to decentralize the collaborative filtering computations. Many large-scale decentralized systems use an interconnection network overlay over the Internet, where individual nodes in the system connect to some set of other nodes, and to achieve scalability in this case it is widely accepted that gossiping models are the most effective choice. For collaborative filtering in these systems, the nodes themselves are typically considered to be ”users”, that are searching for information, and the similarity between users can be directly instantiated in the overlay network, where similar users are directly connected to each other. On this basis, recommendation becomes straight forward, because neighboring nodes will likely have relevant information. We call this Similarity Based Clustering (SBC) of an overlay network. In this thesis, we improve upon the state-of-the-art approach for gossip-based SBC overlays, by proposing a new protocol, called Optimum Benefit Protocol, that outperforms existing protocols in terms of key metrics including convergence time, bandwidth consumption and node churn. We have implemented our protocol on PeerNet, a simulation environment for large-scale decentralized systems, and obtained a range of results using real-world data sets drawn from Movielens, Yahoo WebScope and Epinions, that provide user profiles, user ratings, and item information from big data sources. Specifically our approach addresses the ”long-tail iii convergence” problem observed where some overlay networks take a very long time to converge for some applications. Overall we show that our protocol is competitive for building large-scale decentralized systems that require collaborative filtering. iv Declaration This is to certify that 1. the thesis comprises only my original work towards the PhD degree, 2. due acknowledgement has been made in the text to all other material used, 3. the thesis is less than 100,000 words in length, exclusive of tables, maps, bibliographies and appendices. Irum Fahim Bukhari, March 2018 v Acknowledgements I would like to express my deepest gratitude to my supervisor, Dr. Aaron Harwood, for his patient guidance, enthusiastic encouragement and useful critiques of this research work. Aaron has provided me with key insights and valuable suggestions to help me establish the main direction for this research. I have learnt from him the invaluable characteristics of a top researcher: always being persevere and paying attention to the detail. I also thank Dr. Egemen Tanin for his support and advice as the chair of my Advisory Committee. I would like to thank Dr. Shanika Karunasekera for her advice and assistance in keeping my progress on schedule. She has helped me greatly in reviewing my written reports and articles. As an experienced researcher in the field of peer to peer networks, she has provided me with valuable insights and suggestions to help me achieve high quality research outcomes. I would like to thank my family, especially my husband who has always encouraged me to pursue higher study. I could not complete my PhD without his understanding, constant support and encouragement during my PhD candidature. My daughters and my son have always been the main source of inspiration and motivation that helped me go through difficult times during the study. I am also thankful to my parents and my parents-in-law for their endless support, especially my mother-in-law who came over to Australia to help me when I gave birth to my daughter during my PhD candidature. Finally, I am thankful to the Department of Computing and Information Systems for supporting me throughout my PhD candidature. vii Author’s Publications Journal Papers 1. Irum F. Bukhari, Aaron Harwood and Shanika Karunasekera, Optimum Benefit Protocol: A Fast Converging, Bandwidth-Efficient Decentralized Similarity Overlay, Journal of Parallel and Distributed Computing, Vol. 109, pp. 129-141, 2017. Conference Papers 1. Irum F. Bukhari, Aaron Harwood and Shanika Karunasekera, ”Effect of Similarity Distribution on the Convergence of Decentralized Similarity Overlays”, in Proc. 13th Australasian Symposium on Parallel and Distributed Computing, pp. 13-22, January 27-30, 2015. 2. Irum F. Bukhari, Aaron Harwood and Shanika Karunasekera, ”Handling Churn in Similarity Based Clustering Overlays Using Weighted Benefit”, in Proc. 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, December 18-20, 2017. ix