Document

On the Cooling-aware Workload
Placement Problem
Paolo Cremonesi – Andrea Sansottera
Dipartimento di Elettronica e Informazione
Politecnico di Milano
Goal
• Datacenter consolidation
2
Goal
• Datacenter consolidation
• Performance aware: takes into account QoS and
performance requirements
3
Goal
• Datacenter consolidation
• Performance aware: takes into account QoS and
performance requirements
• Power aware: minimizes server power
consumption
4
Goal
• Datacenter consolidation
• Performance aware: takes into account QoS and
performance requirements
• Power aware: minimizes server power
consumption
• Cooling aware: minimizes total datacenter power
consumption
5
Outline
•
•
•
•
•
Introduction
Linear Heat Flow Model
Validation of the Linear Heat Flow Model
Optimal Workload Placement
Future Work
AAAI 2011
Cremonesi, Gualandi, Sansottera
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Motivations
• Power aware workload placement prioritizes the
most power efficient servers
– This prioritization might lead to hotspots: a very
power efficient server might be placed in a bad
position
– In order to meet thermal specification of the servers,
the temperature of the cold air must be lowered
– The power consumption of the cooling devices
increases
• Ignoring cooling might lead to overall inefficiency
AAAI 2011
Cremonesi, Gualandi, Sansottera
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Data Center Power Model
PDC  PSERVERS  PCRAC
PSERVERS   Ps
sS
PCRAC
PSERVERS

COP
Coefficient of Performance (COP):
heat removed over work provided
AAAI 2011
Cremonesi, Gualandi, Sansottera
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Outline
•
•
•
•
•
Introduction
Linear Heat Flow Model
Validation of the Linear Heat Flow Model
Optimal Workload Placement
Future Work
AAAI 2011
Cremonesi, Gualandi, Sansottera
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The Linear Heat Flow Model
• We want to determine if the air supplied by the
CRAC is cold enough to guarantee thermal
thresholds at the server inlets
– Computational Fluid Dynamics (CFD)
AAAI 2011
Cremonesi, Gualandi, Sansottera
10
Data center layout
Thermo-Fluid Dynamic models
12
Results depend on where the workload is placed
The Linear Heat Flow Model
• We want to determine if the air supplied by the
CRAC is cold enough to guarantee thermal
thresholds at the server inlets
– Computational Fluid Dynamics (CFD)
– We need to test many power distributions due to the
exponential number of possible workload placements
– CFD too computationally expensive
– The is to create a simplified linear heat flow model
– Model parameters are estimated with few CFD
simulations
14
The Linear Heat Flow Model
• Two basic assumptions
– All power drawn by a server is dissipated as heat
– Thermal exchange between the server interior and
the computer room happens through convection
15
The Linear Heat Flow Model
Recirculation
Hot air flow
Hot air re-circulates
Active CRAC
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The Linear Heat Flow Model
• Fundamental assumption: the fraction of heat recirculating from one server to another is mostly
dependent on the layout
Fraction of cold air flow to server i
Tin,i


  a j ,iTout, j  1   a j ,i Tsup
jS
 jS

Fraction of air flow from server j to server i (cross-interference coeff.)
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Outline
•
•
•
•
•
Introduction
Linear Heat Flow Model
Validation of the Linear Heat Flow Model
Optimal Workload Placement
Future Work
18
Validation: data center layout
• Two rows of 5 racks in a
cold-hot aisle layout
• Each rack contains 4
servers/enclosures
• Two CRACs (one active)
• Cold air exits from the
raised floor in the cold aisle
CRAC (off)
Cold
Aisle
5
10
4
9
3
Hot
Aisle
8
2
7
1
6
Cold
Aisle
CRAC (on)
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Validation results (power): 40 servers
Scenario
1
2
Error mean [K]
0.3604
0.3760
Error std. dev. [K]
0.3059
0.3485
3
4
5
6
0.4642
0.5125
1.4174
1.4827
0.3360
0.3667
1.0032
1.0306
• Racks are full
– 40 servers/enclosures
– 40 CFD simulations for each of the 6 scenarios
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Outline
•
•
•
•
•
Introduction
Linear Heat Flow Model
Validation of the Linear Heat Flow Model
Optimal Workload Placement
Future Work
21
Problem Statement
•
•
•
•
Optimal assignment of workloads to servers
Minimize power consumption: servers + CRAC
Heterogeneous servers
Performance (response time and throughput)
constraints
– Queuing network models
• Thermal constraints
– Linear heat flow model maps workload placement
with optimal cold air temperature
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Results: power savings (10 servers)
Num. work.
Utiliz.
Power
aware
Cooling
aware
20
0.3
14.97 %
15.87 %
20
0.5
11.77 %
17.32 %
20
0.7
8.75 %
16.90 %
40
0.3
15.13 %
16.65 %
40
0.5
11.74 %
16.41 %
40
0.7
12.31 %
15.42 %
100
0.3
14.97 %
15.42 %
100
0.5
9.89 %
14.51 %
100
0.7
6.99 %
9.58 %
• CRAC power consumption matters, especially with high power
density
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Results: power savings (40 servers)
Num. work.
Utiliz.
Cooling
aware
80
0.3
19.09 %
80
0.5
23.75%
80
0.7
27.29 %
160
0.3
18.96 %
160
0.5
23.68 %
160
0.7
28.42 %
400
0.3
18.68 %
400
0.5
24.04 %
400
0.7
28.29 %
• 18%-29% power savings with respect to a load balancing strategy
(in this case, optimal cold air temperature is set a-posteriori)
• As utilization grows, greater savings can be achieved
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Outline
•
•
•
•
•
Introduction
Linear Heat Flow Model
Validation of the Linear Heat Flow Model
Optimal Workload Placement
Future Work
25
Future Work
• Heat flow model
– From a linear model to a statistical model
– Impact of turning off the fans
• Optimization
– Integration of fault tolerance constraints
– More complex performance constraints
• Multiple resources (memory, storage, network)
• Virtualization modeling
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Thank you
AAAI 2011
Cremonesi, Gualandi, Sansottera
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