How to save the world   from big data Tactics for making cloud computing          massively greener      Francine Bennett ...
Run anywhere    batch jobslatency insensitivestandard hardware
Them: “While this sounds like an interestingproject, Im afraid we dont make thesesorts of data points available.”Me: “OK, ...
Electricity source Emissions factor  Data centre  Power Usage  Effectiveness     Server Standard power  consumption
Electricity source Emissions factor    Data centre       Physical building and layout   Power Usage        Occupancy level...
Kg CO2e per KWh of national grid electricity 1.00 0.75 0.50 0.25   0                                 Sweden               ...
Physical designUtilisationLocal climatePower source
Physical design   1.6UtilisationLocal climatePower source
Physical design   1.6Utilisation       1.2Local climatePower source
Physical design   1.6Utilisation       1.2Local climate     2.0Power source
Physical design   1.6Utilisation       1.2Local climate     2.0Power source      8.0
Occupancy                  6%                    Physical design                         13%Power source                Cl...
0                         0.15                                0.30                                       0.45             ...
Myth 1:efficient == green
© 2006 Bruno & Lígia Rodrigues
pic of geothermal                    © ThinkGeoEnergy
Myth 2:  efficiencyimprovements   solve it
William Stanley Jevons
Tell  yoursuppliers
Demand   andshare data
© 2006 Bruno & Lígia Rodrigues
© ThinkGeoEnergy
Thank you.Francine Bennett (@fhr)      Mastodon C Big Data Done Better
Strata london slide deck
Strata london slide deck
Strata london slide deck
Strata london slide deck
Strata london slide deck
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Strata london slide deck

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Slides from my talk at Strata London 2012.
How to Save the World from Big Data: Tactics for Making Cloud Computing Massively Greener

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Strata london slide deck

  1. 1. How to save the world from big data Tactics for making cloud computing massively greener Francine Bennett (@fhr) Mastodon C
  2. 2. Run anywhere batch jobslatency insensitivestandard hardware
  3. 3. Them: “While this sounds like an interestingproject, Im afraid we dont make thesesorts of data points available.”Me: “OK, no problem. Is there any publicindustry data which you regard as highquality, which you would recommend if weremaking estimates? Also would be great toknow if [Company] has any green initiativeswhich we should be aware of in ourmodelling.”Them: “None that Im aware of, but wecontinually and relentlessly seek even moreefficient operations.”
  4. 4. Electricity source Emissions factor Data centre Power Usage Effectiveness Server Standard power consumption
  5. 5. Electricity source Emissions factor Data centre Physical building and layout Power Usage Occupancy levelEffectiveness (PUE) External temperature & humidity Hardware Standard power consumption
  6. 6. Kg CO2e per KWh of national grid electricity 1.00 0.75 0.50 0.25 0 Sweden UK China Switzerland US (average) Australia Iceland Source: Defra via Amee
  7. 7. Physical designUtilisationLocal climatePower source
  8. 8. Physical design 1.6UtilisationLocal climatePower source
  9. 9. Physical design 1.6Utilisation 1.2Local climatePower source
  10. 10. Physical design 1.6Utilisation 1.2Local climate 2.0Power source
  11. 11. Physical design 1.6Utilisation 1.2Local climate 2.0Power source 8.0
  12. 12. Occupancy 6% Physical design 13%Power source Climate 61% 20%
  13. 13. 0 0.15 0.30 0.45 0.60 Iceland Oregon London Winter Dublin Virginia Texas Iceland Oregon LondonSummer Dublin Virginia Footprint of one server hour Texas
  14. 14. Myth 1:efficient == green
  15. 15. © 2006 Bruno & Lígia Rodrigues
  16. 16. pic of geothermal © ThinkGeoEnergy
  17. 17. Myth 2: efficiencyimprovements solve it
  18. 18. William Stanley Jevons
  19. 19. Tell yoursuppliers
  20. 20. Demand andshare data
  21. 21. © 2006 Bruno & Lígia Rodrigues
  22. 22. © ThinkGeoEnergy
  23. 23. Thank you.Francine Bennett (@fhr) Mastodon C Big Data Done Better

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