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Design and Analysis of an Energy Agile Cluster Computing SystemAndrew Krioukov, Prashanth Mohan, Stephen Dawson-Haggerty, Sara Alspaugh, David Culler, Randy Katz

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Grid Evolution

SUPPLIES

LOADS

mostly dispatchable

renewable, variable, intermittent, greatly non-dispatchable

oblivious, stochastic, mostly non-power proportional

reactive, mostly power proportional

TODAY IDEAL FUTURE

oblivious, flat

OLD GRID

non-renewable, reactive, dispatchable

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Grid Evolution

SUPPLIES

LOADS

mostly dispatchable

renewable, variable, intermittent, greatly non-dispatchable

oblivious, stochastic, mostly non-power proportional

reactive, mostly power proportional

TODAY IDEAL FUTURE

oblivious, flat

OLD GRID

non-renewable, reactive, dispatchable

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Grid Evolution

SUPPLIES

LOADS

mostly dispatchable

renewable, variable, intermittent, greatly non-dispatchable

oblivious, stochastic, mostly non-power proportional

power proportional, reactive, grid-aware

TODAY IDEAL FUTURE

oblivious, flat

OLD GRID

non-renewable, reactive, dispatchable

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Grid

Internet

SUPPLIES:provide powercommunicate renewable availability, price

LOADS:adapt demand

communicate forecast

electricity

information

Pieces Needed

?

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Non-dispatchable, variable supply

Power proportional, grid-aware loads

NREL Western Wind and Solar Integration Study Datasethttp://wind.nrel.gov/Web_nrel/

Pacheco wind farm

Scientific computing cluster

Figure of merit: amount of wind used.How do we get here?

Renewable Integration

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POW

ER

TIME

oblivious, flat load

dispatchable supply

power proportionality

grid-awareness

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Data Center Loads

IT Equip-ment 61%

Cooling30%

Power Cond.8%

Light1%

data center consumption dominated by IT loadIT load driven by workload

need power proportionalityneed load shaping mechanism

Server Idle:Peak

HP ProLiant DL160

63.5%

Apple XServe 3.1 51.8%IBM System x3450

51.6%

Dell PowerEdge 2950

57.9%

Pelley, et. al, Understanding and Abstracting Total Data Center Power, 2009Barroso et. al. The Case for Energy-Proportional Computing, 2007

SPECpower Results http://www.spec.org/power_ssj2008/results/power_ssj2008.html

5,000 servers at Googleaverage 30% utilization

IT equipment is not power proportional

powe

r (W

)

utilization

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Power Proportionality

Spinning Reserve

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Architecture

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Outline• Motivation• Enabling technology• Methodology• Algorithms• Evaluation

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Renewable Energy Component

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Formulation

We assume the wind farm is sized for the data center.

Option 1: grid blend (open system)

Wind

Other

Requires assuming load is negligible fraction of grid – not realistic

Option 2: dedicated wind farm (closed system)

Fit load to specific wind farm

http://www.greenhousedata.com/

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Wind

Wind power over 48 hours from a wind farm in Monterrey County, California.

Variation in wind power for month long intervals at multiple wind farms.

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Workload Component

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Workloads

Torque jobs

Num

Jobs

Batch: Less latency sensitive, longer jobse.g., analytics, scientific computing

Requ

est R

ate

Wikipedia traffic

Interactive: Latency sensitive, generally short jobse.g., web app server, email server, etc.

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Slackslack = max run time – job duration

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Cluster: NERSC FranklinAverage duration: 98 minAverage slack: 68 min

Cluster: EECS PSIAverage duration: 55 minAverage slack: 17 hours

Slack in Real Systems

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Grid-Aware Batch Scheduling• example goal:

shape load to match wind availability

• method: exploit temporal slack

Pacheco wind farm

Scientific computing cluster

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Greedy AlgorithmB(t) = power budget for next 10 min

Sort jobs by slack

Schedule all jobs with no remaining slack

Schedule other eligible jobs in least-remaining-slack order until B(t) is exceeded

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Run-immediately, grid-oblivious scheduler

Greedy, grid-aware scheduler

Grid-aware scheduling increases wind energy use.

Correspondingly, reduces grid dependence.

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Reduction in grid dependence is robust to choice of wind farm.

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As slack increases, grid dependence diminishes.

PSI Franklin

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Grid-aware scheduling is equivalent to 5 hours worth of data center-sized batteries.

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Grid-aware scheduling is equivalent to 5 hours worth of data center-sized batteries.

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Summary• Power proportionality and grid-aware

scheduling• Energy savings, renewable integration,

grid stabilityreduce grid dependence by halfequivalent to 5 hours of batteries

• Next stepsslack in other systems...?

QUESTIONS?THE END

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