design and analysis of an energy agile cluster computing system
DESCRIPTION
Design and Analysis of an Energy Agile Cluster Computing System. Andrew Krioukov , Prashanth Mohan, Stephen Dawson-Haggerty, Sara Alspaugh , David Culler, Randy Katz. Grid Evolution. renewable, variable, intermittent, greatly non-dispatchable. non-renewable, reactive, dispatchable. - PowerPoint PPT PresentationTRANSCRIPT
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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...?
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QUESTIONS?THE END
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