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GreenSlot : Scheduling Energy Consumption in Green Datacenters

GreenSlot : Scheduling Energy Consumption in Green Datacenters. Íñigo Goiri , Kien Le, Md. E. Haque , Ryan Beauchea , Thu D. Nguyen, Jordi Guitart , Jordi Torres, and Ricardo Bianchini. Motivation. Datacenters consume large amounts of energy Energy cost is not the only problem

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GreenSlot : Scheduling Energy Consumption in Green Datacenters

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  1. GreenSlot: Scheduling Energy Consumption in Green Datacenters ÍñigoGoiri, Kien Le, Md. E. Haque, Ryan Beauchea, Thu D. Nguyen, JordiGuitart, Jordi Torres, and Ricardo Bianchini

  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 • Connect datacenters to green sources • Solar panels, wind turbines… • Green datacenter

  3. Green datacenter • Energy sources • Solar/wind: variable availability over time • Electrical grid: backup • Other (problematic) approaches • Batteries: losses, cost, environmental • Bank energy on the grid: losses, cost, unavailability Wind Power Solar Power Time

  4. Scheduling scientific workloads • Batch jobs • User specifies: #nodes, estimated runtime, deadline • Challenge • Match workloads with green energy availability Power Load Time

  5. GreenSlot • 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 • Turn off idle servers to save energy

  6. Dealing with energy costs • Schedule jobs: evaluate energy cost • Green energy is “free” (amortization): $0.00/kWh • Cheap (off peak, 11pm to 9am): $0.08/kWh • Expensive (on peak, 9am to 11pm): $0.13/kWh • Optimization goal • Minimize energy cost while meeting deadlines

  7. Conventional vsGreenSlot J3 J2 Power Nodes J1 J3 J3 J2 Power Nodes J1 J3 J1 J2 Now Time

  8. GreenSlot: scheduling round • Divide “scheduling window” into slots (15 minutes) • Predict green energy availability • Consider jobs by earliest start deadline • Calculate cost starting at every slot • Schedule job at the cheapest slot • Dispatch actions • Calculate and start required servers • Start jobs to be executed now • Deactivate unneeded servers (ACPI S3 state) Power Time

  9. GreenSlot: scheduling round • Divide “scheduling window” into slots (15 minutes) • Predict green energy availability • Consider jobs by earliest start deadline • Calculate cost starting at every slot • Schedule job at the cheapest slot • Dispatch actions • Calculate and start required servers • Start jobs to be executed now • Deactivate unneeded servers (ACPI S3 state) 10 5 0 0 0 5 10 15 X X Power Time

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

  11. 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

  12. 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

  13. 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

  14. Evaluation methodology • Cluster with 16 nodes • Modified version of SLURM • GreenSlot implemented on top • Energy profile • NJ electricity pricing (on/off peak) • Solar farm energy availability (10 panels) • Four weeks (most, best, average, and worst) • Schedulers • Conventional: EASY backfilling • GreenSlot: Green energy, Brown electricity price

  15. Evaluation methodology • Workload • Real workload from BSC • Workflows for sequencing yeast genome • 5 days (Monday to Friday) • Deadlines: 9am, 1pm, and 4pm Monday Tuesday Wednesday Thursday Friday

  16. Energy prediction vs actual

  17. GreenSlot for BSC workload Conventional 26 kWh 75 kWh $8.00 24% cost savings 38 kWh 63 kWh $6.06 -24% GreenSlot

  18. GreenSlot for BSC workload

  19. Other results • Impact of weather miss-predictions • Less than 1% cost savings • Workloads variations: Staggered and Multi-node • Consistent green energy increases and cost savings • Workload intensity (datacenter utilization) • Works well with low/medium utilization • High switches to conventional • Inaccurate user run time estimations • Maximum cost increase of 2%

  20. Staggered workload Conventional 32 kWh 69 kWh $8.58 30% cost savings 38 kWh 63 kWh $6.00 -30% GreenSlot

  21. Conclusions • Parallel job scheduler for green datacenters • Predicts green energy availability • Increases the use of green energy • Reduces energy related costs • Solar array amortized in 11 years (18 years originally) • We are building a solar-powered μDatacenter

  22. GreenSlot: Scheduling Energy Consumption in Green Datacenters ÍñigoGoiri, Kien Le, Md. E. Haque, Ryan Beauchea, Thu D. Nguyen, JordiGuitart, Jordi Torres, and Ricardo Bianchini

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