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Computing for the Future of the Planet Andy Hopper

Computing for the Future of the Planet Andy Hopper. A. Hopper and A. Rice, “Computing for the Future of the Planet”, Phil. Trans. R. Soc. A , Oct 2008. http://www.cl.cam.ac.uk/research/dtg/research/wiki/CFTFP. The Computer Laboratory University of Cambridge.

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Computing for the Future of the Planet Andy Hopper

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  1. Computing for theFuture of the PlanetAndy Hopper A. Hopper and A. Rice, “Computing for the Future of the Planet”, Phil. Trans. R. Soc. A, Oct 2008. http://www.cl.cam.ac.uk/research/dtg/research/wiki/CFTFP The Computer Laboratory University of Cambridge

  2. Computing for the Future of the Planet • Optimal Digital Infrastructure • Sense and Optimise • Predict and React • Digital Alternatives to Physical Activities

  3. 1 – Optimal Digital Infrastructure Source: Data Center Efficiency in the Scalable Enterprise, Dell Power Solutions, Feb 2007 28% 31% 41% 26% of overall energy 63% 22% 15% 8% of overall energy • Energy in Data Centres

  4. Utilisation of Servers L.A. Barroso and U. Hö̈lzle, The Case for Energy-Proportional Computing, IEEE Computer 40, 33–37. (DOI 10.1109/MC.2007.443.)‏

  5. Power and Load – Multiple Servers • Tasks are moved between machines • Machines not in use are switched off • Non-interactive jobs are delay tolerant • data indexing, batch simulation, climate modeling • XEN virtualisation approximates energy proportional computing • move on 10G Ethernet in 250msec down time, 10sec migration time

  6. Use of Remote Energy Sources Sun Siemens press picture • Keep moving computing tasks to where energy is available • Cheaper to transmit data rather than energy • Use energy that cannot be used for another purpose • At what granularity should jobs be shipped? • Do we ship program, data, or both?

  7. Migration Performance (10G Ethernet) S. Akoush, A. Rice, R. Sohan, A. Moore 7

  8. The Overall Goal • Optimal Digital Infrastructure • Components switched off if not doing useful work • Energy proportional computing and communications at many levels • Use of energy that is not suitable for other purposes • Components • Servers / Server Farms • Networks • Workstations • Terminals • For the first time over-provisioning may not save the day!

  9. 2 - Sense and Optimise • A sensor-based digital model of the planet • “Googling” Earth! • “Googling” Space-Time! • “The Power of Data” • How do we do it? • coverage • fidelity • scalability • performance • usefulness

  10. From Identification to Precise Location UWB RTLS Passive RFID Active RFID / WiFi Barcode Presence Detection Presence Detection Identification Presence Detection Presence Detection Location Location Precise Location Precise Location manual Auto ID 3m – 10m 1m – 3m < 1m

  11. Location Sensing Indoors • Ultra wideband radio location system • Measure pulse time-differences-of-arrival and angles-of-arrival

  12. World Model A real-world environment where people are wearing location tags A 3D rendering of a “World Model” constructed and updated in real time using location and other systems

  13. Programming with Space • Spatial index: • Define spaces • Monitor interactions • Generate events • Containment • Overlapping • +ve and –ve events

  14. BMW Plant Installation facts: 1.7 km line | Sensors: 350 | Tags: 1,000 |Accuracy: 30 cm in 3D | Events/day: 150,000 | Reliability: 99.99%

  15. VBL Bus Depot Luzern Installation facts: Area: 15,000 m² in 1 depot | Sensors: 48 | Accuracy: 100 cm in 2D | Reliability: 99.9%

  16. Sensing Outdoors - Use of Vehicles, Aircraft, Mobiles, Humans

  17. More GPS readings Fewer GPSreadings Converting GPS data to Road Maps J. Davies • GPS location data converted into a directed graph of the road network

  18. Mapping the Spectrum D. Cottingham • Measured 3G signal strength • Red is poor reception Blue is excellent Orange circles are base stations RFeye™ DC-6GHz Node

  19. Converting RSS Data to Coverage Maps Need to convert thousands of points to ~20, per road D. Cottingham, R. Harle

  20. Personal Energy Meter - PEM S. Hay, A. Rice • Information about individual energy consumption • Measured, shared, embedded • Use World Model • upload own energy use to help digital optimisation • download energy profile of devices and goods • Lots of lovely computing problems! • measurement, indexing, caching, event-delivery, prediction, use of social networking, security, privacy, correctness, …

  21. Principles of PEM Apportionment • Complete • all energy accounted for • Accurate / bounded / personalised • my actions relate to me only • Sensible • incentives work correctly • Trustworthy • rules are understood: for the Active Badge these were reciprocity, availability • fidelity / error bounds • security / privacy - “bad” things cannot happen 21

  22. Power consumption varies with occupancy

  23. Policy can penalise some working patterns User A User B User C

  24. Better policy provides correct incentives

  25. OpenRoomMap: crowd-sourced maps of buildings A. Rice, O. Woodman

  26. 3 - Predict and React • Requirements • Accurate and correct model • Algorithm separated from implementation, verified code • Shared data, latest data • Deadline/parameter driven computation • Scalable computer power

  27. Epidemiological model: Citrus Canker A. Rice, C. Gilligan, Defeng Wu, M. Dingley Frequency of tree inspection looking for disease Size of surrounding area to purge when an infected tree is found

  28. More is to come!

  29. 4 - Shift to Cyberspace • Can we construct a digital world in which we can conduct our lives? • on a ultra-cheap open platform • using miniscule power • fed with sensor data from the real-world • accessible to every human • Key to wealth creation in the Developing World?

  30. Developing World: Digital Platform • Capacity • Fibre links to the world • Wireless networks • 3G/EDGE • WiMax • Femtocells • Cheap, commodity hardware: • (Smart)phones ($5) • Netbooks ($70) • Low-cost applications

  31. Developing World: Thoughts • Local support for internet services (optimum digital infrastructure) • Majority of core internet services still hosted outside the continent • Urbanisation is advantageous • Opportunity to use renewable/haphazard energy sources • Traffic congestion (predict and react) • Large (African) cities have underdeveloped road networks • Modelling enables efficient capacity planning • Road conditions (sense and optimise) • Road conditions in Africa can change in a short period • Opportunity for agile re-planning • Global trading (physical to digital)

  32. How can-might-should-will this evolve?

  33. Computing for the Future of the Planet • Targets the world outside (and inside) computing • A vision and an architecture • Lists, dimensions, and quantifies computing problems • Contemplates the unbounded upside of computing!

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