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GreenSlot: Scheduling Energy Consumption in Green Datacenters Íñigo Goiri, Kien Le, Md. E. Haque, Ryan Beauchea, Thu D. Nguyen, Jordi Guitart, Jordi Torres,

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Presentation on theme: "GreenSlot: Scheduling Energy Consumption in Green Datacenters Íñigo Goiri, Kien Le, Md. E. Haque, Ryan Beauchea, Thu D. Nguyen, Jordi Guitart, Jordi Torres,"— Presentation transcript:

1 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

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 Time Solar Power

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

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 J3 Conventional vs GreenSlot J1 J2 J3 J2 J3 Nodes Power Nodes Power Time J3J1J2 Now J1

8 GreenSlot: scheduling round Time Power 1.Divide “scheduling window” into slots (15 minutes) 2.Predict green energy availability 3.Consider jobs by earliest start deadline – Calculate cost starting at every slot – Schedule job at the cheapest slot 4.Dispatch actions – Calculate and start required servers – Start jobs to be executed now – Deactivate unneeded servers (ACPI S3 state)

9 1.Divide “scheduling window” into slots (15 minutes) 2.Predict green energy availability 3.Consider jobs by earliest start deadline – Calculate cost starting at every slot – Schedule job at the cheapest slot 4.Dispatch actions – Calculate and start required servers – Start jobs to be executed now – Deactivate unneeded servers (ACPI S3 state) XX GreenSlot: scheduling round Time Power

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

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

12 J1 J4 J3 GreenSlot behavior J2 Time J2 J1 J3 Nodes Power J4 Schedule: J4Weather prediction was wrong Brown electricity price Job deadline Scheduling window Now

13 J1 J4 J5J3 GreenSlot behavior J2 Time J2 J1 J3J5 Nodes Power J4 J5 Schedule: Brown electricity price Job deadline Scheduling window Now J5

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 MondayTuesdayWednesdayThursdayFriday

16 Energy prediction vs actual

17 GreenSlot for BSC workload Conventional GreenSlot 26 kWh 75 kWh $ kWh 63 kWh $ % 24% cost savings

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 GreenSlot 32 kWh 69 kWh $ kWh 63 kWh $ % 30% cost savings

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 Íñigo Goiri, Kien Le, Md. E. Haque, Ryan Beauchea, Thu D. Nguyen, Jordi Guitart, Jordi Torres, and Ricardo Bianchini


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