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Edge10 Workshop on Princeton Edge Lab’s 10 th

Edge10 Workshop on Princeton Edge Lab’s 10 th. Mung Chiang May 17, 2019. Outline. Ten years ago … Smart Data Pricing Edge for Pricing Pricing the Edge Edge/Fog SCALE Interfaces Fogonomics Dispersive Learning. Acknowledgments. Postdocs, students, visitors Collaborators

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Edge10 Workshop on Princeton Edge Lab’s 10 th

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  1. Edge10Workshop on Princeton Edge Lab’s 10th Mung Chiang May 17, 2019

  2. Outline • Ten years ago… • Smart Data Pricing • Edge for Pricing • Pricing the Edge • Edge/Fog • SCALE • Interfaces • Fogonomics • Dispersive Learning

  3. Acknowledgments • Postdocs, students, visitors • Collaborators • Funding agencies • Industry partners

  4. 0. Ten Years Ago…

  5. Research • Bridget theory-practice gaps in networking • Proofs to prototypes • Edge/Fog (technological networks) • Smart Data Pricing (economic networks) • Social Learning Networks (social networks)

  6. Education • 2011: Network20Q & flip classroom • 2012: MOOC (Chris) 400,000 students • 2012: “Networked Life” • 2016: “Power of Networks” (Chris)

  7. Startups • 2013: DataMi (Sangtae, Carlee, Soumya) 60 million users • 2014: Zoomi (Sangtae, Ruediger, Chris) • 2015: Smartiply (Junshan, Kaushik) • 2017: Myota (Jaeyoon, Sangtae)

  8. Industry and Community Impact • About a dozen company partners • 2015: OpenFog Consortium • 2018: Industrial Internet Consortium • Major conference panels, workshops, industry forums • Special journal/magazine issues and 2 edited books on Fog & SDP • ~50 postdocs/Ph.D. students, ~25 as faculty and ~25 in industry

  9. 1. Smart Data Pricing

  10. SDP Dimensions • How? Usage-based, demand response … real-time … • Whom? Toll-free(1-800, zero rating, sponsored data, split billing)… • What? App-based (no data plan), cloud pricing… IoT pricing, PMP… • More Offloading, Quota-aware preloading…B2B, roaming, peering…

  11. Example: Time Elasticity of Applications Movies & Multimedia downloads, P2P Streaming videos, Gaming Large Peak-Valley Differential Software Downloads Opportunities Cloud Texting, Weather, Finance Email, Social Network updates Opportunities for Exploiting time-elasticity of demand

  12. Cost-effective Mobile Content Delivery Reduce peak & increase valley Defer capital spending Sell unused capacity Increase revenue

  13. Edge Complements Cloud SDK

  14. Rethink Spectrum Flashy Whitespace

  15. Rethink Ecosystem • Stop (just) counting bytes and start living with QoE • Recognize, leverage heterogeneity of apps and networks • Win – Win – Win • Consumers: more choices and lower $/GB • Carriers: higher revenue and lower cost • Content and app providers: more engaged eyeballs

  16. Rethink Networks End User Cellular Core Smart sharing in APP + PHY Mobile management from the edge

  17. Pricing 5G • Spectrum allocation/auctions for new bands of licensed and unlicensed spectrum • Infrastructure sharing:  given densification, how will resource sharing work between competitive operators? • Pricing of consumer mobile • Pricing for broadband access • Pricing of industrial IoT • How will these pricing options evolve when killer apps emerge and mmWave devices become affordable?

  18. Pricing IoT • How to charge? • Time-dependent? Volume discounts? • Application-dependent pricing: pricing with guarantees on delivered outcome or experience, e.g. price 5G network slices with guaranteed QoS • Whom to charge? • Stakeholders include IoT service provider (e.g. smart home sensors), IoT wireless access provider (e.g AT&T), and IoT cloud platforms (e.g. Amazon AWS IoT Hub) • Whom should users pay?Users pay each separately vs users pay only service provider vs … • Vertical integration of stakeholders • What happens if AT&T or Verizon offer both IoT management platforms and connectivity? (they do; Verizon ThingSpace and AT&T Control Center)

  19. 2. Edge/Fog

  20. 2009 Distribute functions to network edge

  21. 2015 2018 Distribute functions along Cloud-2-Things Continuum

  22. To Fog or Not to Fog: SCALE • Security • Cognition • Agility • Latency • Efficiency

  23. Fog as An Architecture • Architecture is “Horizontal Foundation”: • Who does what, at what timescale, how to glue them together? • Allocation of functions, not just resources • Architecture supports Applications: • Source-channel separation: Digital communication • TCP/IP: Internet applications • Fog/edge: IoT / 5G / Dispersive AI

  24. A. Interfaces • Massive storage • Heavy duty computation Global coordination • Wide-area connectivity Real time processing Rapid innovation Client-centric Edge resource pooling

  25. Example: Shred and Spread • Client-driven data processing for privacy protection and reliability • Scatter files to multiple fog storages • Client-side data deduplication • Obfuscated data in storages File chunkingfor data deduplication Chunk encoding/spreadingfor privacy and reliability

  26. Example: Networked Drone Cameras

  27. B. Fogonomics • Compute price: • Memory size • Compute time • Data storage S S • Communication price: • Requests across functions • Data transmission • Internet access • Incentivizing local dispersed resources: • Cellular data plans • User mobility pattern • Heterogeneous devices • Network connections

  28. Application-Dependent Pricing The specific application offered changes resources and pricing • Example: Pricing of data collected by edge devices • Optimal amount and frequency of charging • Pricing based on measures of freshness of data • How to price and sell private data? • Example: Pricing of distributed ML services • Using fog/edge resources for distributed ML to make inferences, find correlations, or for online planning

  29. C. Edge/Fog for Dispersive AI • Designmachine learning algorithms that support fast responses • Decompose machine learning into multiple geographically distributed components (jointly operating to adaptively optimize data collection/analytics) • Minimize communication costs and centralized data processing costs • Make best use of local/proximal resources • Proactively pre-position content and computing • Parallelize successive refinement for streaming mining • Reduce infrastructure costs and improve quality of experience

  30. Dispersive Learning • Decentralized, online decision making under uncertainty by a team of edge devices in an unknown environment • Examples: fleet of drones deployed for anti-poaching efforts, team of disaster relief robots • Solution approach: multi-agent reinforcement learning, augmented with inter-agent communication for better learning and coordination • Information shared by the informed devices with others could in fact degrade their learning early on • Delayed sharing may be preferred: wait until policies have improved, then share

  31. Timing Matters P. Naghizadeh, M. Gorlatova, A. Lan, M. Chiang. “Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning”, INFOCOM 2019. Information sharing might help learning… Or might degrade it!

  32. Unique Challenges & Opportunities • Heterogeneity/Under-organization of resources/devices • Variability/Volatility in availability/mobility • Constraints in bandwidth/battery • Proximity to sensors/actuators

  33. Thank you & To the next 10 yearschiang@purdue.educhiangm@princeton.edu

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