GreenSlot: Scheduling Energy Consumption in Green Datacenters

Transcription

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 Solar Power 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

4 Load Scheduling scientific workloads Batch jobs User specifies: #nodes, estimated runtime, deadline Challenge Match workloads with green energy availability Power 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 Nodes Nodes Conventional vs GreenSlot J3 J2 Power J1 J3 J2 Power Now J1 Time J3 J1 J2

8 GreenSlot: scheduling round 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 Power Time 4. Dispatch actions Calculate and start required servers Start jobs to be executed now Deactivate unneeded servers (ACPI S3 state)

9 GreenSlot: scheduling round 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 X X Power Time 4. Dispatch actions Calculate and start required servers Start jobs to be executed now Deactivate unneeded servers (ACPI S3 state)

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

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

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

13 Nodes GreenSlot behavior Schedule: J5 J2 J4 Power J1 J3 J5 Now Time J5 Brown electricity price 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 Error (%) Energy (kwh) Energy prediction vs actual Prediction Actual Hours ahead

17 GreenSlot Conventional GreenSlot for BSC workload 26 kwh 75 kwh $ % cost savings 38 kwh 63 kwh $ %

18 % GreenSlot for BSC workload Most Best Average Worst Green energy increase Cost savings

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 GreenSlot Conventional Staggered workload 32 kwh 69 kwh $ % cost savings 38 kwh 63 kwh $ %

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