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People-level network analytics

People-level network analytics. Monitor user workload characteristics Tailor (reprogram) network based on this. Nishanth Sastry King’s College London. Why this should be on EPSRC’s agenda?. Why now [Timeliness  ] New closed loops allow people-level analytics

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People-level network analytics

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  1. People-level network analytics Monitor user workload characteristics Tailor (reprogram) network based on this Nishanth Sastry King’s College London

  2. Why this should be on EPSRC’s agenda? • Why now [Timeliness ] • New closed loops allow people-level analytics • IPTV, Catch-up TV, shortened URLs (bit.ly), Google analytics… • Social media as URL launchers == “analytics plane” • Networks programmable as never before (SDN) • Why UK [Novelty and ambition ] • strong in reprogramming of networks • (all of session one!) • Strong in analytics/measurements • (several in this room itself – you know who you are!) • Bring them together and make a difference!

  3. People-Analytics Driven Network Designs • Social network information for content placement • For timing push of news updates: TailGate (WWW’12) • Selective replication:Scellato et al (WWW’11), Buzztraq • Easy to predict what you watch on Catch-up! • Speculative recording of people’s favouriteprogrammes can halve BBC iPlayer network footprint (WWW’13) • Adult video - people flexible on what to watch, so long as they have not seen it before (IMC’13) • Need to replace most watched vids for returning users! • Need to reconsider LRU policies in this setting

  4. Research issues • Integrating people-level info with flow-level programmability and virtualisation constructs • Granularity of “people-level” info different from SDN • Need new programming models/adapters • Reliability of data/noisiness • Heterogeneity (Heavy users/light users, different geographical densities) • Extending to support cellular networks • Programmability is a completely different ball game • Machinecharacteristics-driven M2M networks?

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