Energy-efficient data centers: Exploiting knowledge about application and resources

“Ingeniamos el futuro”!
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Energy-efficient data centers:
Exploiting knowledge about
application and resources
José M. Moya
Integrated Systems Laboratory
José M.Moya | Madrid (Spain), July 27, 2012 1

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Data centers

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Power distribution

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Power distribution (Tier 4)

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Contents
•  MoHvaHon
•  Our approach
–  Scheduling and resource
management
–  Virtual machine
opHmizaHons
–  Centralized management
of low-‐power modes
–  Processor design
•  Conclusions

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Motivation
•  Energy consumpHon of data centers
–  1.3% of worldwide energy producHon in 2010
–  USA: 80 mill MWh/year in 2011 = 1,5 x NYC
–  1 data center = 25 000 houses
•  More than 43 Million Tons of CO2
emissions per
year (2% worldwide)
•  More water consumpHon than many industries
(paper, automoHve, petrol, wood, or plasHc)
Jonathan Koomey. 2011. Growth in Data center electricity use 2005 to 2010

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Motivation
•  It is expected for total data
center electricity use to
exceed 400 GWh/year by
2015.
•  The required energy for
cooling will conHnue to be at
least as important as the
energy required for the
computaHon.
•  Energy opHmizaHon of future
data centers will require a
global and mulH-‐disciplinary
approach.
0
5000
10000
15000
20000
25000
30000
35000
2000 2005 2010
World server installed base
(thousands)
High-‐end servers
Mid-‐range servers
Volume servers
0
50
100
150
200
250
300
2000 2005 2010
Electricity use
(billion kWh/year)
Infrastructure
CommunicaHons
Storage
High-‐end servers
Mid-‐range servers
Volume servers
5,75 Million new servers per year
10% unused servers (CO2
emissions
similar to 6,5 million cars)

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Temperature-dependent
reliability problems
Time-‐dependent
dielectric-‐
breakdown (TDDB)
ElectromigraHon (EM)
Stress
migraHon (SM)
Thermal
cycling (TC)
✔
✖
✖
✖

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Cooling a data center

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•  VirtualizaHon

-‐ 27%
•  Energy Star server
conformance

= 6.500
•  Bejer capacity
planning

2.500
Server improvements
. . . . . . . . . . . . . . . . . . . . . . . . . . .
Servidores
5,75 millones de nuevos servido-
res se instalan cada año para
mantenerse al ritmo de creci-
miento de los servicios on-line, y
todavía aproximadamente el 10%
de los servidores instalados no
se utilizan debido a sobrestima-
ciones conservadoras a la hora
de planificar las necesidades de
almacenamiento.
La energía utilizada para
los servidores en desuso
podría compensar
las emisiones de 6,5
millones de coches.
de los servidores.
En un centro de datos convencional, algunas de
estas mejoras pueden reducir su impacto en las
siguientes cifras:
(excluyendo el software)
$ $ $ $
A menudo los servidores se sobredimensionan
para afrontar picos de demanda, lo que significa
que como media suelen funcionar sólo al 20%
2.500
6,5 millones
= 6.500
El equivalente a retirar 6.500
coches de las carreteras, me-
diante la utilización de servidores
acordes a Energy Star, lo que re-
duciría el consumo eléctrico de
los centros de datos en 82.000
megavatios-hora.
10% no se utilizan
US
27% Reducir un 27% el consumo
energético mediante la virtuali-
zación, lo que reduce la capaci-
dad productiva no empleada.
El equivalente a la energía consumida por 2.500
hogares en EEUU, mediante una mejor planifica-
ción de la capacidad.

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Cooling improvements
•  Improvements in air ﬂow management and
wider temperature ranges
Energy savings
up to 25%
25.000
Return of investment
in only 2 years

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AC è DC
– 20% reducHon of power losses in the
conversion process
– 47 million dollars savings of real-‐state costs
– Up to 97% eﬃciency, energy saving enough to
power an iPad during
70
million years
Infrastructure improvements

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Best practices

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And…
what about IT people?

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PUE
Power Usage Effectiveness
•  State of the Art: PUE ≈ 1,2
–  The important part is IT energy consumpHon
–  Current work in energy eﬃcient data centers is
focused in decreasing PUE
–  Decreasing PIT
does not decrease PUE, but it is seen in
the electricity bill
•  But how can we reduce PIT
?
!"# =
1
!"#$
=
!!"!#$
!!"
=!
!
!!!!!!!!!!=
!!"#$%
+ !!""#$%&
+ !!"#
!!"#$%
≈
!!""#$%&
+ !!"
!!"
!

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Potential energy savings
by abstraction level

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Our approach
•  Global strategy to allow the use of mulHple
informaHon sources to coordinate decisions in order
to reduce the total energy consumpHon
•  Use of knowledge about the energy demand
characterisHcs of the applicaEons, and
characterisHcs of compuEng and cooling resources
to implement proacEve opEmizaEon techniques

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Holistic approach
Chip Server Rack Room MulE-‐
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5

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1. Room-level resource
management
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5

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Leveraging heterogeneity
•  Use heterogeneity to minimize energy
consumpHon from a staHc/dynamic point of view
–  StaEc: Finding the best data center set-‐up, given a
number of heterogeneous machines
–  Dynamic: OpHmizaHon of task allocaHon in the
Resource Manager
•  We show that the best soluHon implies an
heterogeneous data center
–  Most data centers are heterogeneous (several
generaHons of computers)
21
CCGrid 2012
José M.Moya | Madrid (Spain), July 27, 2012
M. Zapater, J.M. Moya, J.L. Ayala. Leveraging Heterogeneity for
Energy MinimizaHon in Data Centers, CCGrid 2012

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Current scenario
22
WORKLOAD
Scheduler Resource
Manager
ExecuEon

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Potential improvements
with best practices
ot aisle
0
200
400
600
800
1000
1200
1400
0 20 40 60 80 100
Power (KW)
job size relative to data center capacity (%)
Total power (computing and cooling) for various scheduling approaches
savings by minimizing computing power
savings by minimizing the recirculation’s effect
savings by turning off idle machines
unaddressed heat recirculation cost
basic (unavoidable) cost
max computing power, worst thermal placement
min computing power, worst thermal placemenit
optimal computing+cooling
optimal computing+cooling, shut off idles
optimal computing+cooling, shut off idles, no recirculation
Fig. 3. Data center operation cost (in kilowatts) for various “savings
modes”. Savings are based on heat recirculation data obtained by

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Cooling-aware scheduling and
resource allocation
0
50
100
150
200
FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT
energy consumed (GJ)
Energy consumption, Scenario (a) 40 jobs, 25014 core-hours, idle servers on
Throughput
Turnaround time
Alg. runtime
Energy savings
0.197 jobs/hr 0.197 jobs/hr 0.172 jobs/hr 0.197 jobs/hr 0.163 jobs/hr
18.41 hr 18.41 hr 20.75 hr 18.41 hr 51.75 hr
3.4 ms 6.9 ms 213 ms 23 min 40 min
0% 6.2% 8.6% 8.7% 10.2%
cooling energy
computing energy
(a)
0
5
10
15
20
25
30
35
40
Energy consumption, Scenario (a) 40 jobs, 25014 core-hours, idle servers off
Throughput
Turnaround time
Alg. runtime
Energy savings
18.41 hr 18.41 hr 20.75 hr 18.41 hr 38.02 hr
3.4 ms 6.9 ms 213 ms 23 min 43 min
0% 11.8% 54.7% 21.8% 60.5%
cooling energy
computing energy
(b)
0
50
100
150
200
250
300
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on
Throughput
Turnaround time
Alg. runtime
Energy savings
8.98 hr 8.98 hr 12.17 hr 8.98 hr 48.49 hr
170 ms 186 ms 397 ms 40.8 min 88.6 min
0% 1.7% 4.1% 3.6% 4.7%
cooling energy
computing energy
(c)
0
5
10
15
20
25
30
35
40
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off
Throughput
Turnaround time
Alg. runtime
Energy savings
8.98 hr 8.98 hr 12.17 hr 8.98 hr 17.75 hr
171 ms 186 ms 397 ms 42 min 100 min
0% 4.0% 14.6% 14.2% 15.1%
cooling energy
computing energy
(d)
300
350
400
450
(GJ)
Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers on
Throughput
Turnaround time
Alg. runtime
Energy savings
9.99 hr 9.99 hr 13.39 hr 9.99 hr 65.38 hr
173 ms 196 ms 346 ms 20 min 142 min
0% 2.5% 5.9% 9.4% 12.5%
cooling energy
computing energy
80
100
(GJ)
Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers off
Throughput
Turnaround time
Alg. runtime
Energy savings
9.99 hr 9.99 hr 13.39 hr 9.99 hr 61.49 hr
173 ms 191 ms 346 ms 21 min 147 min
0.0% 7.5% 17.3% 25.7% 41.4%
cooling energy
computing energy
0
50
100
150
Turnaround time
Alg. runtime
Energy savings
18.41 hr 18.41 hr 20.75 hr 18.41 hr 51.75 hr
3.4 ms 6.9 ms 213 ms 23 min 40 min
0% 6.2% 8.6% 8.7% 10.2%
(a)
0
5
10
15
20
25
30
Throughput
Turnaround time
Alg. runtime
Energy savings
18.41 hr 18.41 hr 20.75 hr 18.41 hr 38.02 hr
3.4 ms 6.9 ms 213 ms 23 min 43 min
0% 11.8% 54.7% 21.8% 60.5%
(b)
0
50
100
150
200
250
300
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on
Throughput
Turnaround time
Alg. runtime
Energy savings
8.98 hr 8.98 hr 12.17 hr 8.98 hr 48.49 hr
170 ms 186 ms 397 ms 40.8 min 88.6 min
0% 1.7% 4.1% 3.6% 4.7%
cooling energy
computing energy
(c)
0
5
10
15
20
25
30
35
40
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off
Throughput
Turnaround time
Alg. runtime
Energy savings
8.98 hr 8.98 hr 12.17 hr 8.98 hr 17.75 hr
171 ms 186 ms 397 ms 42 min 100 min
0% 4.0% 14.6% 14.2% 15.1%
cooling energy
computing energy
(d)
0
50
100
150
200
250
300
350
400
450
Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers on
Throughput
Turnaround time
Alg. runtime
Energy savings
9.99 hr 9.99 hr 13.39 hr 9.99 hr 65.38 hr
173 ms 196 ms 346 ms 20 min 142 min
0% 2.5% 5.9% 9.4% 12.5%
cooling energy
computing energy
(e)
0
20
40
60
80
100
Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers off
Throughput
Turnaround time
Alg. runtime
Energy savings
9.99 hr 9.99 hr 13.39 hr 9.99 hr 61.49 hr
173 ms 191 ms 346 ms 21 min 147 min
0.0% 7.5% 17.3% 25.7% 41.4%
cooling energy
computing energy
(f)
Fig. 8. Energy comparison of the simulated schemes for the three scenarios. The plots correspond in respective positions to the plots of Figure 7.
policy used in the data center, which enables job execution as soon as they arrive if the queue is empty and the data
center is lightly loaded. In the “idle-on” case (Figure 8a), the total energy consumption using SCINT, EDF-LRH,
iMPACT Lab (Arizona State U)

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Application-aware scheduling and
resource allocation
25
LSI-UPM
WORKLOAD
Resource
Manager
(SLURM)
ExecuEon
Proﬁling and
ClassiﬁcaEon
Energy
OpEmizaEon

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resource allocation
•  Workload:
–  12 tasks from SPEC CPU INT 2006
–  Random workload composed by 2000 tasks, divided into
job sets
–  Random job set arrival Hme
•  Servers:
26
Scenario

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resource allocation
27
Energy profiling
WORKLOAD
Resource
Manager
(SLURM)
ExecuEon
Profiling and
ClassificaEon
Energy
OpEmizaEon
Energy profiling

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Workload characterization

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resource allocation
29
Optimization
WORKLOAD
Resource
Manager
(SLURM)
ExecuEon
Proﬁling and
ClassiﬁcaEon
Energy
OpEmizaEon
Energy MinimizaEon:
•  MinimizaEon subjected to constraints
•  MILP problem (solved with CPLEX)
•  StaEc and Dynamic

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resource allocation
30
Static optimization
•  DeﬁniHon of opHmal data center
–  Given a pool of 100 servers of each kind
–  1 job set from workload
–  The opHmizer chooses the best selecHon of servers
–  Constraints of cost and space
Best soluEon:
•  40 Sparc
•  27 AMD

Savings:
•  5 a 22% energy
•  30% Eme

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resource allocation
31
Dynamic optimization
•  OpHmal workload allocaHon
–  Complete workload (2000 tasks)
–  Good enough resource allocaHon in terms of energy (not
the best)
–  Run-‐Hme evaluaHon and opHmizaHon
Energy savings
ranging from 24%
to 47%

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resource allocation
•  First proof-‐of-‐concept regarding the use of
heterogeneity to save energy
•  AutomaHc soluHon
•  AutomaHc processor selecHon oﬀers notable energy
savings
•  Easy implementaHon in real scenarios
–  SLURM Resource Manager
–  RealisHc workloads and servers
32
Conclusions

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2. Server-level resource
management
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5

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Scheduling and resource allocation
policies in MPSoCs
A. Coskun , T. Rosing , K. Whisnant and K. Gross "StaHc and dynamic temperature-‐
aware scheduling for mulHprocessor SoCs", IEEE Trans. Very Large Scale Integr. Syst.,
vol. 16, no. 9, pp.1127 -‐1140 2008
Fig. 3. Distribution of thermal hot spots, with DPM (ILP).
A. Static Scheduling Techniques
We next provide an extensive comparison of the ILP based
techniques. We refer to our static approach as Min-Th&Sp.
As discussed in Section III, we implemented the ILP for min-
imizing thermal hot spots (Min-Th), energy balancing (Bal-
En), and energy minimization (Min-En) to compare against
Fig. 4. Distribution of spatial gradients, with DPM (ILP).
hot spots. While Min-Th reduces the high spatial differentials
above 15 C, we observe a substantial increase in the spatial
gradients above 10 C. In contrast, our method achieves lower
and more balanced temperature distribution in the die.
In Fig. 5, we show how the magnitudes of thermal cycles vary
with the scheduling method. We demonstrate the average per-
Fig. 3. Distribution of thermal hot spots, with DPM (ILP).
A. Static Scheduling Techniques
We next provide an extensive comparison of the ILP based
techniques. We refer to our static approach as Min-Th&Sp.
As discussed in Section III, we implemented the ILP for min-
imizing thermal hot spots (Min-Th), energy balancing (Bal-
Fig. 4. Distribution of spatial gradients, with DPM (ILP).
hot spots. While Min-Th reduces the high spatial differentials
above 15 C, we observe a substantial increase in the spatial
gradients above 10 C. In contrast, our method achieves lower
and more balanced temperature distribution in the die.
In Fig. 5, we show how the magnitudes of thermal cycles vary
UCSD – System Energy Efficiency Lab

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Scheduling and resource allocation
policies in MPSoCs
•  Energy characterizaHon of applicaHons allows
to deﬁne proacHve scheduling and resource
allocaHon policies, minimizing hotspots
•  Hotspot reducHon allows to raise cooling
temperature
+1oC means around 7% cooling energy savings

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3. Application-aware and
resource-aware virtual machine
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5

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JIT compilation in
virtual machines
•  Virtual machines compile
(JIT compilaHon) the
applicaHons into naHve
code for performance
reasons
•  The opHmizer is general-‐
purpose and focused in
performance
opEmizaEon

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Back-‐end
JIT compilation for
energy minimization
•  ApplicaHon-‐aware compiler
–  Energy characterizaHon of applicaHons and
transformaHons
–  ApplicaHon-‐dependent opHmizer
–  Global view of the data center workload
•  Energy opHmizer
–  Currently, compilers for high-‐end processors oriented
to performance opHmizaHon
Front-‐end
OpHmizer Code generator

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Energy saving potential for
the compiler (MPSoCs)
T. Simunic, G. de Micheli, L. Benini, and M. Hans. “Source code opHmizaHon and
proﬁling of energy consumpHon in embedded systems,” InternaHonal Symposium on
System Synthesis, pages 193 – 199, Sept. 2000
– 77% energy reducHon in MP3 decoder
FEI, Y., RAVI, S., RAGHUNATHAN, A., AND JHA, N. K. 2004. Energy-‐opHmizing source
code transformaHons for OS-‐driven embedded soyware. In Proceedings of the
InternaHonal Conference VLSI Design. 261–266.
– Up to 37,9% (mean 23,8%) energy savings in
mulHprocess applicaHons running on Linux

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4. Global automatic
management of low-power
modes
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5

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DVFS – Dynamic Voltage
and Frequency Scaling
•  As supply voltage decreases, power decreases
quadraHcally
•  But delay increases (performance decreases)
only linearly
•  The maximum frequency also decreases
linearly
•  Currently, low-‐power modes, if used, are
acHvated by inacHvity of the server operaHng
system

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Room-level DVFS
•  To minimize energy consumpHon, changes
between modes should be minimized
•  There exist opHmal algorithms for a known
task set (YDS)
•  Workload knowledge allows to globally
schedule low-‐power modes without any
impact in performance

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Parallelism to save energy
Use of Parallelism
Use of Parallelism
V
dd
V
dd
/2 V
dd
/2
f f /2 f /2
f
max
f
max
/2 f
max
/2
9-17
Swiss Federal
Institute of Technology
Computer Engineering
and Networks Laboratory

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5. Temperature-aware floorplanning of
MPSoCs and many-cores
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3
architecture 4 4
technology 5

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Temperature-aware
floorplanning

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Potential energy savings
with floorplanning
–  Up to 21oC reducHon of maximum temperature
–  Mean: -‐12oC in maximum temperature
–  Bejer results in the most criHcal examples
Temperature Reductions
Average MaxTemp reduction: 12 oC
Larger temperature reductions for benchmarks
with higher maximum temperature
For many benchmarks, temperature reducions are
larger than 20 oC
Maximum Temperature
0
20
40
60
80
100
120
140
ammp
applu
apsi
art
bzip2
crafty
eon
equake
facerec
fma3d
gap
gcc
gzip
lucas
mcf
mesa
mgrid
parser
perlbmk
swim
twolf
vortex
vpr
wupwise
avg
original modified
Y. Han, I. Koren, and C. A. Moritz. Temperature Aware Floorplanning. In Proc. of the
Second Workshop on Temperature-‐Aware Computer Systems, June 2005

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Temperature-aware
floorplanning in 3D chips
•  3D chips are gezng interest due to:
–  ↑ ↑ Scalability: reduces 2D equivalent
area
–  ↑ ↑ Performance: shorter wire length
–  ↑ Reliability: less wiring

•  Drawback:
–  Huge increment of hotspots
compared with 2D equivalent designs

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Temperature-aware
floorplanning in 3D chips
•  Up to 30oC reducHon per layer in a 3D chip
with 4 layers and 48 cores

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There is still much more
to be done
•  Smart Grids
– Consume energy when everybody else does not
– Decrease energy consumpHon when everybody
else is consuming
•  Reducing the electricity bill
– Variable electricity rates
– ReacHve power coeﬃcient
– Peak energy demand

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Conclusions
•  Reducing PUE is not the same as reducing energy
consumpHon
–  IT energy consumpHon dominates in state-‐of-‐the-‐art data
centers
•  ApplicaHon and resources knowledge can be effecHvely
used to define proacEve policies to reduce the total energy
consumpHon
–  At different levels
–  In different scopes
–  Taking into account cooling and computaHon at the same Hme
•  Proper management of the knowledge of the data center
thermal behavior can reduce reliability issues
•  Reducing energy consumpHon is not the same as reducing
the electricity bill

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Contact
José M. Moya
+34 607 082 892
[email protected]

ETSI de Telecomunicación, B104
Avenida Complutense, 30
Madrid 28040, Spain
Gracias:

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Energy-efficient data centers: Exploiting knowledge about application and resources

Energy-efficient data centers: Exploiting knowledge about application and resources

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