Presentation by Josué Pagán at DCIS 2013 conference, organized by CEIT (Nov 27th, 2013)

1. A Cyber Physical Approach to
Combined HW-SW Monitoring for
Improving Energy Efficiency in
Data Centers
Josué Pagán, Marina Zapater, Oscar Cubo, Patricia
Arroba, Vicente Martín and José M. Moya
Universidad Politécnica de Madrid
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2. Contents
1. Power consumption problem in Data Centers
I. Introduction
II. Related work
2. Optimization Framework & Data analysis
I. A Cyber-physical system
II. Data analysis and sensor configuration
3. Results
4. Conclusions
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3. Data Center. Consumption
1. POWER CONSUMPTION
PROBLEMS IN DATA CENTERS
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4. 1. Power consumption
problems in Data Centers
• The numbers of the energy problem:
– DC world power consumption >1.3%
– In urban areas >50% of DC exceeds power grid capacity
– USA: 80 TWh/year in 2011 = 1.5 x NY
Power  >600 TWhr expected in
2015 in the global footprint
•
Data Centers’ power consumption
is unsustainable
Projection of total electricity use by datacenters in the US and the world based on Koomey’s and EPA’s data
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5. 1. Power consumption
problems in Data Centers
• Related work (approaches)
Cooling
• Allow the room temperature to increase
• Longer task → cooler server
• Balancing workload between servers
Computation • Reducing voltage/ frequency (DVFS)
– These two approaches are not enough individually
Holistic
(IT+cooling)
• Room environment affects (environmental monitoring)
• Measuring server, workload and environmental variables
to improve energy efficiency → usage of a CPS
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6. 1. Consumption problems in
Data Centers
Requirements
Energy optimization
Our contributions
Make a holistic optimization
framework including environmental,
server and workload information
Dynamically adapt on runtime to
workload and environment
Gather, monitor and analyze in real
time
Gather useful data at the appropriate
rate
In a non-intrusive way, reducing the
data collected with an adaptable
sampling rate
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7. Cyber-Physical System. Data acquisition
2. OPTIMIZATION FRAMEWORK
& DATA ANALYSIS
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8. 2. Optimization Framework &
Data analysis
• One step ahead. Optimization
– 80% Wpeak – 30% of workload (↓η)
GATHER
DATA
PROPOSE
OPTIMIZATIONS
GENERATE
KNOWLEDGE
– An energy model supposes apply optimizations over the Data Center
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9. 2. Optimization Framework &
Data analysis
• Monitoring
– How a Data Center works?
– 30-50% cooling→ energy optimization
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10. 2. Optimization Framework &
Data analysis
• What measure and why
– Environmental monitoring
 Inlet and outlet temperature
 Differential pressure
– Server monitoring
Server consumption, CPU temperature, fan speed
• …to predict
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11. 2. Optimization Framework &
Data analysis
• How…
• exploring sampling intervals
– Temperature and power values
for AMD server under the
benchmark SPEC CPU 2006
– Different sampling rates for
different parameters
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12. 2. Optimization Framework &
Data analysis
• Using… Multilevel star topology architecture
WSN
- Reconfigurable low -power:
only useful data without
information loss
- Adapt to changes in the
environment
RM
- Spatio-temporal allocation
- Possibly to change
decisions if needed
Gateway
-Fan-less, managed with a light OS
-Receive, store, analyze and convert
data. Establishes a timestamp.
-Sends data to the opt. platform
Server Sensors
- Internal sensors
- Polled via SW
Air conditioning
- Exhaust
temperature,
RH% and airflow
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13. 3. RESULTS
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14. 3. Results
WSN deployment
•
Applied over Magerit Supercomputer in CeSViMa Supercomputing and Visualization Center
of Madrid
•
Cluster  9 racks  260 servers
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15. 3. Results
– The goal: develop techniques to allow energy optimization in real environments
– With reconfigurable sampling rate:
– we achieve up to 68 % of reduction in gathered data
– Increase the WSN’ s life time depending on the occupancy
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16. 4. CONCLUSIONS
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17. 4. Conclusions
Energy efficiency has to be faced in a holistic way
We propose an optimization framework monitoring environmental,
server and workload parameters
After a first monitoring study: a WSN has been deployed to gather
environmental data
Up to a 68% of reduction in the amount of gathered data
Maximizing the life time of WSN nodes
Solution applied in a real case study
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18. Thank you for your attention
FIN
This project has been funded in part by the INNPACTO LPCLOUD: "Optimal Management Of low-power modes in cloud computing" IPT2012-1041-430000, developed in collaboration with Elite Ermestel and Converging Technologies and the CDTI project CALEO:
Distribution of operational thermal and optimization of energy consumption in data centers, "developed in collaboration with INCOTEC.
The author gratefully acknowledges the computer resources, technical expertise and assistance provided by the Supercomputing and
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Visualization Center of Madrid (CeSViMa).

19. 4. Results and Conclusions
• Results: gathering data
• Inlet and outlet temperature
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20. Magerit Supercomputer
• Cluster  9 racks  260 servers
 245 are IBM PS702 2S
o 16 Power7 processors @ 3.3 GHz
o 32 GB of RAM
 15 are IBM HS22
o 8 Intel Xeon processors @ 2.5 GHz
o 96 GB of RAM
 200 TB of storage
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21. Industry
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22. Software
• Pasarela
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23. Cyber-Physical System
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24. Psychrometric chart
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25. Differential pressure and
airflow
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26. Installing nodes [6T+1H]
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