minimization of costs and energy consumption in a data ... · minimization of costs and energy...

Minimization of Costs and Energy Consumption in a Data Center by a

Workload-based Capacity Management

Georges Da Costa1, Ariel Oleksiak2,4, WojciechPiatek2, Jaume Salom3, Laura Siso3

1IRIT, University of Toulouse2Poznan Supercomputing and Networking Center 3IREC, Institut de Recerca en Energia de Catalunya4Poznan University of Technology

E2DC, Cambridge, 10/06/14 1

Outline

• Data center model

• Workload-based dynamic power capping

• Workload-based dynamic power capping for variable power supply


Problem and motivation• Capacity management

– Finding such DC configuration that space, power and cooling capacity is maximized

• Additional goals– Minimization of energy use, OPEX, CAPEX

• Issues– Capacity management based on server

nameplate leads to overprovisioning

• The approach– Capacity management based on workload,

tuned by dynamic power capping

• DC model that include both workload and cooling needed


DATA CENTER MODELA holistic approach to simulate data center


5

Integrated analysis of software, IT

equipment, and cooling

Workload & Resource Simulation

Workload & Resource Simulation

CFD SimulationCFD Simulation

Metrics Calculation

Metrics Calculation

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Linpack 4c

Daemon outputReal output

E2DC, Cambridge, 10/06/14

Hardware & Software Modeling

Hardware & Software Modeling

Power use modeling – IT

PCPU = Pidle+ PCii=1

n

å PC = PmaxCLC100

PPXCPU (L)= PPXidle+ (P

PXmax -P

PXidle)

L

100


PNODE = PCPUi=1

l

å +PRAM + PNETj=1

m

å

PNODE_GROUP = PNODEi=1

l

å + PFANj=1

m

å

PRACK = ( PNODE_GROUPi=1

n

å +c) /hPSU• Rack

• Node group (e.g. blade center)

• Node (server)

• Processor

• Core (if power and load are known)

Power use modeling

PDATA_CENTER = PRACKi=1

n

å +PFANSDC +PCOOLING +POTHERS

POTHERS =a * PRACKi=1

n

å

PFANSDC =Dp*Vairtotal

h f


fh - efficiency of fans

α – percentage of power usedby UPS, PDU, lighting, etc.

Cooling models


• EER improves with higher inlet temp (TR_in)

• EER improves with higher cooling capacity (Qcooling_rated)

Power use modeling – cooling


chillerP (t)= coolingQ (t)

EER(t)

EER - Energy Efficiency Ratio for a chiller

EER(t) ~Tev Tev =TR_ in -DTh-ex

EER(t) ~1

PLR(t) PLR(t)=Qcooling(t)

Qcooling_nom

Qcooling_nom ~Qcooling_ rated

WORKLOAD-BASED DYNAMIC POWER CAPPING

An approach to reduced energy use, OPEX and CAPEX


Power capping


• Power capping: ensuring that overall power use of a system does not exceed given thresholds

• Supported by hardware and software (DCIM) vendors (P-States and clock throttling)

• Various levelsand types of capping (e.g. HP)

Workload-based dynamic power capping

• Adaptation to workload by– Dynamic

power capping

– Cooling management (temp.)

• Set power caps to– Avoid

increase of energy use by IT

– Keep mean completion time below threshold


Actual peak power

Theoretical peak power

Minimizing energy consumption by power capping


i

excess

E = max(0,i

IT

P1t

2t

ò (t)-iPC )dt

i

reserve

E = max(0,i

IT

iPC -P1t

2t

ò (t))dt

i

excess

E <i

reserve

E

Power capping algorithm


Power capping algorithm, part2ś


Power capping algorithm, part2


EXPERIMENTS AND RESULTSSimulation studies


Simulation experimentsThree cases:• Experiment A: Load Balancing strategy, reference

case• Experiment B: Workload-based Power Capping,

allowing server inlets up to 27°C (servers far from CRAC)

• Experiment C: Workload-based Power Capping, allowing server inlets up to 27°C (servers far from CRAC), Smaller cooling capacity used: 180[kW]

Workload:• Nr of tasks: 1280 batch rendering tasks• Load: Mean ~ 25% [0% - 75%]• Arrival rate: According to 8 different Poisson

distributions– Overall mean ~ 7s [1s – 205s]


Simulation experiment• Eight racks real

server room– 4414 cores

• Case based on rendering farm

• CFD simulations applied to check the CRAC outlet temp. increase


Simulation results

0

50

100

150

200

250

Mean rackpower

Meanpower

Max rackpower

Max power

[kW]Power

A

B

C

0

2000

4000

6000

8000

Mean completion time Mean task executiontime

[s]Time

A

B

C

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600

Total energy consumption

[kWh] Energy

A

B

C

0

20

40

60

80

100

Total cooling device energy consumption

[kWh] Energy

A

B

C


Simulation results – Metrics• Cooling

energy reduction– by 38%

• PUE decrease– by 5%

• Total energy use– by 4%


Simulation results – CAPEX• Cooling

CAPEX reduction– up to 25%

• Power infra CAPEX reduce– by 10%

• Cooling + power infra– up to 14%

• Total CAPEX reduction– 4% / 7%


Issues• Limited reduction of energy use caused by:

– Chiller partial load characteristics (EER-PLR curve)

– Simplified model provides lower estimations of savings than real ones

• In the studied case cooling is relatively small part ~15%

• Need to run CFD to investigate detailed impact


WORKLOAD-BASED POWERCAPPING FOR DEMAND-RESPONSE

Reducing energy costs for variable power supply


Power capping for DRM

• Demand-Response Management (DRM):

– Adaptation of DC configuration to changing demand and supply

• Changing prices of energy depending on a period and agreed power use limit

• Power capping as a technique to manage demand and minimize costs


Application to demand-response management

• Regular price for energy: 0.0942/kWh

• Agreement: not exceed 200kW

– Otherwise: the cost of 1 kWh = 0.15/kWh

– Yearly savings of 45k euros

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50

100

150

no power capping mix

[euros] Total energy cost

0

0,05

0,1

0,15


[euro] Average energy price

0

2000

4000

6000

8000


[s]Mean completion time


Conclusions• Holistic model to DC modeling including

workloads and cooling– Along with simulations tools (DCworms)

• Workload-based dynamic power capping led to – Up to 38% reduction of cooling energy and OPEX

reduction (>4% of total)

– Up to 25% decrease of cooling and 14% of cooling and power infrastructure in CAPEX (7% of total)

– ~25% OPEX reduction for dynamic energy prices

• Next steps– Model improvements, validation, other policies


Questions?

35E2DC, Cambridge, 10/06/14

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