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Enhancing Content Distribution in Large-Scale Online Communities

This document explores methods to enhance content distribution for very large-scale online communities. It discusses the challenges faced by large-scale online distribution systems, such as variable workloads, availability, and energy consumption. The main objective is to develop a dual model of data distribution with self-adaptive properties that incorporates load prediction models, improves content organization using access patterns and historical data, and reduces traffic pressure through a P2P-based proxy network. The proposed architecture aims to adapt efficiently to changing conditions.

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Enhancing Content Distribution in Large-Scale Online Communities

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  1. Methodstoenhancecontent-distributionforverlylargescale online communities Juan M. Tirado http://arcos.inf.uc3m.es/~jtirado

  2. Motivation http://arcos.inf.uc3m.es/~jtirado • Largescale online distributionsystemshavetodealwith: • Variable workloads • Spontaneouslydemanded data • Viral propagation • Popularitychangesover time • Availability • Energyconsumption • Total Cost of Ownership (TCO) • An ideal systemadaptstoworkloadvariationssavingenergy and ensuringavailability

  3. Objectives http://arcos.inf.uc3m.es/~jtirado • Todevelop a dual model of data distributionwithself-adaptiveproperties: • Provide load-predictionmodelsautomaticselection, configuration and updating • Leverageaccesspatterns, workloadhistory and social data toimprovecontentorganization • Reduce trafficpressureemploying a P2P-based proxy network

  4. Objectives Users+Data+Adaptability http://arcos.inf.uc3m.es/~jtirado • Todevelop a dual model of data distributionwithself-adaptiveproperties: • Provide load-predictionmodelsautomaticselection, configuration and updating • Leverageaccesspatterns, workloadhistory and social data toimprovecontentorganization • Reduce trafficpressureemploying a P2P-based proxy network

  5. ProposedArchitecture ... http://arcos.inf.uc3m.es/~jtirado

  6. Contributions & publications http://arcos.inf.uc3m.es/~jtirado • Contributions • Predictive data groupingbasedoncontentaffinity • Data placementbasedonworkload and accesspatternprediction • Applyautoregressivemodelstopredictthebehavior of data groups • Publications • Juan M. Tirado, Daniel Higuero, Florin Isaila, Jesus Carretero. Analyzing the impact of events in an online music community. Workshop on Social Network Systems, Eurosys 2011 • Juan M. Tirado, Daniel Higuero, Florin Isaila, Jesus Carretero. Predictive data grouping and placement for cloud-based elastic server infrastructures CCGRID 2011. 29% acceptation rate • Juan M. Tirado, Daniel Higuero, Florin Isaila, Jesus Carretero, Adriana Iamnitchi. Affinity P2P: A self-organizing content-based locality-aware collaborative peer-to-peer network. Computer Networks, Volume 54, Issue 12, Pages 2054-2070. 2010. (Impact Factor: 1.2)

  7. http://arcos.inf.uc3m.es/~jtirado Thanksforyouattention!

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