Ariel Oleksiak

Minimization of Costs and Energy
Consumption in a Data Center by a
Workload-based Capacity Management
Georges Da Costa1, Ariel Oleksiak2,4, Wojciech
Piatek2, Jaume Salom3, Laura Siso3
1IRIT, University of Toulouse
2Poznan Supercomputing and
Networking Center
3IREC, Institut de Recerca en Energia de Catalunya
4Poznan University of Technology
E2DC, Cambridge, 10/06/14
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Outline
• Data center model
• Workload-based dynamic power
capping
• Workload-based dynamic power
capping for variable power supply
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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
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A holistic approach to simulate data center
DATA CENTER MODEL
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Integrated analysis of software, IT
equipment, and cooling
Metrics
Calculation
CFD Simulation
Linpack 4c
460
440
Workload & Resource
Simulation
Power used
420
400
380
360
340
320
300
280
260
Daemon output
Real output
10:58
10:59
10:59
11:00
11:00
11:01
11:01
11:02
11:02
Date\nTime
Hardware & Software
Modeling
5
E2DC, Cambridge, 10/06/14
Power use modeling – IT
• Rack
n
PRACK = (å PNODE _ GROUP + c) / hPSU
i=1
• Node group
(e.g. blade
center)
• Node
(server)
l
m
i=1
j=1
PNODE _ GROUP = å PNODE + å PFAN
l
m
i=1
j=1
PNODE = å PCPU + PRAM + å PNET
• Processor
• Core (if
power and
load are
known)
PX
PX
PX
PX
PCPU
(L) = Pidle
+ (Pmax
- Pidle
)
n
PCPU = Pidle + å PCi
i=1
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PC = PmaxC
L
100
LC
100
6
Power use modeling
n
PDATA_ CENTER = å PRACK + PFANSDC + PCOOLING + POTHERS
i=1
PFANSDC =
Dp*Vairtotal
hf
h
f
- efficiency of fans
n
POTHERS = a * å PRACK α – percentage of power used
i=1
by UPS, PDU, lighting, etc.
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Cooling models
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Power use modeling – cooling
Pchiller (t) =
Q
(t)
cooling
EER(t)
EER - Energy Efficiency Ratio
for a chiller
• EER improves with higher inlet temp (TR_in)
Tev = TR_in - DTh-ex
EER(t) ~ Tev
• EER improves with higher cooling capacity
(Qcooling_rated)
1
EER(t) ~
PLR(t)
Qcooling (t)
PLR(t) =
Qcooling_ nom
Qcooling_ nom ~ Qcooling_ rated
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An approach to reduced energy use, OPEX and CAPEX
WORKLOAD-BASED DYNAMIC
POWER CAPPING
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Power capping
• Power capping:
ensuring that
overall power
use of a system
does not exceed
given thresholds
• Supported by
hardware and
software (DCIM)
vendors (PStates and clock
throttling)
• Various levels
and types of
capping (e.g.
HP)
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Workload-based dynamic power capping
• Adaptation to
workload by
Theoretical peak power
– Dynamic
power
capping
– Cooling
managemen
t (temp.)
Actual peak power
• Set power
caps to
– Avoid
increase of
energy use
by IT
– Keep mean
completion
time below
threshold
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Minimizing energy consumption by
power capping
t2
ò max(0, Pi (t) - PCi )dt
Ei
t1
t2
IT
reserve
=
max(0,
ò
Ei
PCi -Pi (t))dt
t1
excess
=
E
excess
i
IT
reserve
< Ei
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Power capping algorithm
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Power capping algorithm
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Power capping algorithm
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Power capping algorithm
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Power capping algorithm
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Power capping algorithm
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Power capping algorithm, part2ś
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Power capping algorithm, part2
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Power capping algorithm, part2
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Power capping algorithm, part2
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Simulation studies
EXPERIMENTS AND RESULTS
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Simulation experiments
Three 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]
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Simulation experiment
• Eight racks real
server room
– 4414 cores
• Case based on
rendering farm
• CFD simulations
applied to check
the CRAC outlet
temp. increase
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Simulation results
Energy
[kWh]
100
Energy
[kWh]
600
500
80
60
A
B
40
C
20
0
400
A
300
B
200
C
100
0
Total cooling device energy consumption
Total energy consumption
Power
[kW]
Time
[s]
8000
250
200
6000
150
A
100
B
50
C
0
Mean rack
power
Mean
power
Max rack Max power
power
A
4000
B
2000
C
0
Mean completion time
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Mean task execution
time
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Simulation results – Metrics
• Cooling
energy
reduction
– by 38%
• PUE
decrease
– by 5%
• Total
energy
use
– by 4%
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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%
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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
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Reducing energy costs for variable power supply
WORKLOAD-BASED POWER
CAPPING FOR DEMAND-RESPONSE
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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
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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
[euros]
150
Total energy cost
[euro]
0,15
Average energy price
8000
completion time
6000
0,1
100
[s] Mean
4000
50
0,05
2000
0
no power capping
mix
0
0
no power capping
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mix
no power capping
mix
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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
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Questions?
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