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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.

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design and analysis of an energy agile cluster computing system

Design and Analysisof an Energy AgileCluster Computing System

Andrew Krioukov, PrashanthMohan, Stephen Dawson-Haggerty, Sara Alspaugh,David Culler, Randy Katz

grid evolution
Grid Evolution

renewable, variable, intermittent, greatly non-dispatchable

non-renewable, reactive, dispatchable

mostly dispatchable

Supplies

Ideal Future

Old Grid

Today

Loads

oblivious, stochastic, mostly non-power proportional

reactive, mostly power proportional

oblivious, flat

grid evolution1
Grid Evolution

renewable, variable, intermittent, greatly non-dispatchable

non-renewable, reactive, dispatchable

mostly dispatchable

Supplies

Ideal Future

Old Grid

Today

Loads

oblivious, stochastic, mostly non-power proportional

reactive, mostly power proportional

oblivious, flat

grid evolution2
Grid Evolution

renewable, variable, intermittent, greatly non-dispatchable

non-renewable, reactive, dispatchable

mostly dispatchable

Supplies

Ideal Future

Old Grid

Today

Loads

oblivious, stochastic, mostly non-power proportional

power proportional, reactive, grid-aware

oblivious, flat

pieces needed
Pieces Needed

SUPPLIES:

provide power

communicate renewable availability, price

Internet

LOADS:

adapt demand

communicate forecast

Grid

electricity

information

?

renewable integration
Renewable Integration

Non-dispatchable, variable supply

Figure of merit: amount of wind used.

How do we get here?

Power proportional, grid-aware loads

Pacheco wind farm

Scientific computing cluster

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

slide7

dispatchable supply

Power

oblivious, flat load

Time

power proportionality

grid-awareness

data center loads
Data Center Loads

5,000 servers at Google

average 30% utilization

data center consumption dominated by IT load

IT load driven by workload

need power proportionality

need load shaping mechanism

IT equipment is not power proportional

power (W)

utilization

Pelley, et. al, Understanding and Abstracting Total Data Center Power, 2009

Barrosoet. al. The Case for Energy-Proportional Computing, 2007

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

power proportionality
Power Proportionality

Spinning Reserve

outline
Outline
  • Motivation
  • Enabling technology
  • Methodology
  • Algorithms
  • Evaluation
formulation
Formulation

Option 1: grid blend (open system)

Option 2: dedicated wind farm (closed system)

Other

Wind

Requires assuming load is negligible fraction of grid – not realistic

Fit load to specific wind farm

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

http://www.greenhousedata.com/

slide14
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.

workloads
Workloads

Interactive: Latency sensitive, generally short jobs

e.g., web app server, email server, etc.

Wikipedia traffic

Request Rate

Batch: Less latency sensitive, longer jobs

e.g.,analytics, scientific computing

Torque jobs

Num Jobs

slack
Slack

slack = max run time – job duration

slack in real s ystems
Slack in Real Systems

Cluster: NERSC Franklin

Average duration: 98 min

Average slack: 68 min

Cluster: EECS PSI

Average duration: 55 min

Average slack: 17 hours

grid a ware b atch scheduling
Grid-Aware Batch Scheduling
  • example goal: shape load to match wind availability
  • method: exploit temporal slack

Pacheco wind farm

Scientific computing cluster

greedy algorithm
Greedy Algorithm

B(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

grid aware scheduling increases wind energy use
Grid-aware scheduling increases wind energy use.

Run-immediately,

grid-oblivious scheduler

Greedy,

grid-aware scheduler

Correspondingly, reduces grid dependence.

summary
Summary
  • Power proportionality and grid-aware scheduling
  • Energy savings, renewable integration, grid stability

reduce grid dependence by half

equivalent to 5 hours of batteries

  • Next steps

slack in other systems

...?