greenslot: scheduling energy consumption in green datacenters Íñigo goiri, kien le, md. e. haque,...
TRANSCRIPT
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– Brown sources: coal, natural gas…
• Lots of small and medium datacenters• Connect datacenters to green sources– Solar panels, wind turbines…– Green datacenter
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 Pow
er
Time
Sola
r Pow
er
Scheduling scientific workloads
• Batch jobs• User specifies: #nodes, estimated runtime, deadline• Challenge– Match workloads with green energy availability
Power
Time
Load
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
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
GreenSlot: scheduling round
Time
Power
1. Divide “scheduling window” into slots (15 minutes)2. Predict green energy availability3. 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)
1. Divide “scheduling window” into slots (15 minutes)2. Predict green energy availability3. 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)
10 5 0 0 0 5 10 15 X X
GreenSlot: scheduling round
Time
Power
J1
GreenSlot behavior
J2
Time
J1
J2
Now
Nod
esPow
er
J1J2
Schedule:
Brown electricity priceJob deadlineScheduling window
J1, J2
J1J3
J4
GreenSlot behavior
J2
Time
J1
J2
J4
J3
Nod
esPow
er
J3J4
Schedule:
Brown electricity priceJob deadlineScheduling window
Now
J3, J4
J1
J4
J3
GreenSlot behavior
J2
Time
J2
J1J3
Nod
esPow
er
J4
J4
Schedule: J4 Weather prediction was wrong
Brown electricity priceJob deadlineScheduling window
Now
J1
J4
J5J3
GreenSlot behavior
J2
Time
J2
J1J3 J5
Nod
esPow
er
J4
J5
Schedule:
Brown electricity priceJob deadlineScheduling window
Now
J5
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
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
Energy prediction vs actual
6:00 AM
7:00 AM
8:00 AM
9:00 AM
10:00 AM
11:00 AM
12:00 PM
1:00 PM
2:00 PM
3:00 PM
4:00 PM
5:00 PM
6:00 PM
7:00 PM0
0.51
1.52
PredictionActual
Ener
gy (k
Wh)
0 6 12 18 24 30 36 42 480
5
10
15
20
Hours ahead
Erro
r (%
)
GreenSlot for BSC workloadCo
nven
tiona
lG
reen
Slot
26 kWh75 kWh
$8.00
38 kWh63 kWh
$6.06 -24%
24% cost savings
GreenSlot for BSC workload
Green energy increase Cost savings0
20
40
60
80
100
120MostBestAverageWorst
%
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%
Staggered workloadCo
nven
tiona
lG
reen
Slot
32 kWh69 kWh
$8.58
38 kWh63 kWh
$6.00 -30%
30% cost savings
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