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Leveraging Renewable Energy in Data Centers

Leveraging Renewable Energy in Data Centers. Ricardo Bianchini on tour 2012. Motivation. Datacenters consume large amounts of energy Energy cost is not the only problem Brown sources: coal, natural gas… Lots of small and medium datacenters Use renewable sources for datacenters

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Leveraging Renewable Energy in Data Centers

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  1. Leveraging Renewable Energy in Data Centers Ricardo Bianchini on tour 2012

  2. Motivation • Datacenters consume large amounts of energy • Energy cost is not the only problem • Brown sources: coal, natural gas… • Lots of small and medium datacenters • Use renewable sources for datacenters • Solar panels, wind turbines… • “Green" datacenters

  3. Renewable sources has lower environmental impact Wastes last thousands of years

  4. Motivation • Datacenters consume large amounts of energy • Energy cost is not the only problem • Brown sources: coal, natural gas… • Lots of small and medium datacenters • Use renewable sources for datacenters • Solar panels, wind turbines… • “Green" datacenters

  5. Renewable approaches for“green” datacenters • Centralized generation • Utilities install renewable power plants • Green pricing: users pay for a green percentage • Compensation systems • CO2 offsets • Renewable Electricity Credits (RECs) • Distributed generation • Connect to close renewable power plants • Self-generation

  6. Why distributed generation? • Energy independence • Stable costs • Resilient to external failures • Reduce transmission and conversion losses • From 41% to 30% losses (even <5%) • Long lifetime • Installations can be reused • Allow local energy management • Control which energy to use and when

  7. Why solar and wind? • Medium/high availability • Suitable for small/medium installations • Initial cost/W is lower • No wastes • Easy to install • Easy to maintain

  8. Why solar? • Solar PV cost scalability is linear • Small installations have similar $/W as large • Better for distributed generation • Solar availability is higher than wind Wind Solar

  9. Price of PV energy is decreasing • PV energy already cheaper than utility in some locations [1]

  10. Problems and challenges • Require large extensions of land • Bad for large datacenters • Low/medium efficiency • Efficiency lower than 40% • Increasingevery year • Variability • Batteries: losses, economic and environmental cost • Net metering: losses, limited availability • Smarter management

  11. Parasol (remove) • Construction • Structure • Solar panels • Installation • Servers: software • Lessons learned • Not as easy as it seems • Hard to deal with facilities crowd • Setting it on the roof has extra cost • Easier to just put solar panels in the roof and use a regular room • Flexibility for research purposes is hard • Metering everywhere: temperature, power

  12. Parasol • We are building a µDatacenter • Powered by • PV panels • Electricity grid • Batteries • Research framework • Manage solar-powered datacenter • Software to exploit renewable energy • Free cooling

  13. Parasol description • Installed on the roof • Steel structure • Container to host the IT • 10 PV panels: 3 kW • Backup energy systems • Batteries: 32 kWh • Power grid • IT equipment • 2 42U racks • 64 Atom servers (so far) • 2 switches • Cooling system • Free cooling • Air conditioner • Heater

  14. Structure craning and assembling

  15. Container and solar panels

  16. Electrical and cooling

  17. IT equipment

  18. Green systems • Handle renewable energy variability • Smart energy management • GreenSlot [SC‘11] • Schedule batch jobs (SLURM) • GreenHadoop [EUROSYS’12] • Schedule data-processing jobs (MapReduce)

  19. Green systems approach • Predict green energy availability • Weather forecast • Schedule jobs • Maximize green energy use • If green not available, consume cheap brown • May delay jobs but must meet deadlines • Manage data availability • Send to sleep (S3) idle servers to save energy

  20. GreenSlot behavior Schedule: J1, J2 J2 J2 Power Nodes J1 J1 J1 J2 Now Brown electricity price Time Job deadline Scheduling window

  21. GreenSlot behavior Schedule: J3, J4 J2 J2 J4 J4 Power Nodes J3 J3 J1 J1 J3 J4 Now Brown electricity price Time Job deadline Scheduling window

  22. GreenSlot behavior Schedule: J4 Weather prediction was wrong J2 J2 J4 J4 Power Nodes J3 J3 J1 J1 J4 Now Brown electricity price Time Job deadline Scheduling window

  23. GreenSlot behavior Schedule: J5 J2 J2 J4 J4 Power Nodes J3 J3 J5 J5 J1 J1 J5 Now Brown electricity price Time Job deadline Scheduling window

  24. Future directions • Collect data of the data center • Real workloads • Temperatures • ???

  25. Leveraging Renewable Energy in Data Centers Ricardo Bianchini on tour 2012

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