The workshop presented strategies for improving energy efficiency in data centers through proactive and reactive thermal optimization techniques. It highlighted the significant power consumption and carbon footprint associated with data centers, emphasizing the need for a multidisciplinary approach to minimize energy use. Key research goals include achieving substantial reductions in energy consumption and CO2 emissions through joint IT and cooling resource optimization methods.

1. MONCLOA Campus of International
Excellence
Proactive and Reactive Thermal
Optimization Techniques to Improve Energy
Efficiency in Data Centers
Workshop PICATA
Marina Zapater
José L. Ayala, José M. Moya
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2. Presentation
ArTeCS Group
Group of Architecture and Technology of Computing Systems
Facultad de Informática
Universidad Complutense de Madrid
Laboratorio de Sistemas Integrados (LSI)
Departamento de Ingeniería Electrónica
ETSI Telecomunicación
Universidad Politécnica de Madrid
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3. Data Centers
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4. Motivation
• Power consumption in data centers
– 1.3% world energy production in 2010
– USA: 80 billion KWh/year in 2011 = 1.5xNYC
– 250 billion KWh/year in 2010
• More than 43 Million tons of CO2 / year
• More water than paper, automotive, petrol, wood
or plastic industry
Jonathan Koomey. 2011. Growth in Data center electricity use 2005 to 2010
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5. Motivation
World server installed base (thousands)
35000
30000
• It is expected for total data 25000
20000 High-end servers
center electricity use to 15000 Mid-range servers
10000
exceed 400 GWh/year by 5000
Volume servers
2015. 0
2000 2005 2010
• The required energy for 5,75 Million new servers per year
cooling will continue to be at 10% unused servers (CO2 emissions
least as important as the similar to 6,5 million cars)
energy required for the
300
computation. 250
Electricity Use (billion KWh/year)
Infrastructure
200 Communications
• Energy optimization of 150 Storage
100 High-end servers
future data centers will 50 Mid-range servers
require a global and multi- 0 Volume servers
disciplinary approach. 2000 2005 2010
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6. State of the Art Energy Savings
for different abstraction levels
Abstraction level
• Higher levels of
abstraction bring
more benefits
• Application-level
still has to be
explored.
Solutions proposed by the State of the Art
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7. Our perspective
Proactive and reactive
holistic approach
• Using the knowledge about the energy demand of the
applications, the features of the computation and
cooling resources to apply proactive optimization
techniques
• Global strategy to integrate multiple information
sources and coordinate decissions to reduce overall
power consumption.
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8. Our perspective
IT and Cooling power
PTOTAL PIT Pcooling
PTOTAL
PUE
PIT
• State of the Art: PUE ≈ 1,2
– The important part is IT energy consumption
– Current work is focused on decreasing PUE
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9. Our perspective
IT and Cooling power
PTOTAL PIT Pcooling
PTOTAL
PUE
PIT
• Minimize IT power
• Jointly minimize IT and cooling
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10. Minimizing IT Power
Leveraging heterogeneity
• Usage heterogeneity (existance of different servers) to
minimize energy consumption:
– Static: Finding the best data center set-up, given a number of
heterogeneous machines
– Dynamic: optimization of task allocation
M. Zapater, J.M.
Moya, J.L. Ayala.
Leveraging
Heterogeneity for
Energy Minimization in
Data Centers, CCGrid
2012
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11. Minimizing IT power
Application Awareness
Scheduler Resource
WORKLOAD
Manager
Execution
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12. Heterogeneity
Application Awareness
Scheduler Resource
WORKLOAD
Manager
Execution
Profiling and Energy
Classification Optimization
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13. Cooling management
Leakage-cooling tradeoffs at the
server level
• Control of the fan speed of a
server
– Enterprise server: Sparc T3 (256
threads)
– Real measures with server
internal sensors
• We can find an optimum
pointbetween leakage and
cooling to minimize power
Work in collaboration with:
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14. Cooling management
Leakage-cooling tradeoffs at the
server level
Work in collaboration with:
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15. Cooling & IT Joint Opt.
Work in Progress
• Deriving a data room thermal
model to jointly allocate
computational and cooling
resources
– Gathering environmental data
through sensors (WSN)
– Server sensors
– Workload information
• Usage of genetic programming
and genetic algotithms
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16. Holistic aproach
Proactive and reactive techniques
• System that increases the knowledge of the data center
• Real implementation scenario at CeSViMa
• Power optimization in globally distributed systems
GreenDISC Project: HW/SW
Data Center Technologies for Energy
state Optimization Efficiency in Distributed
Decission Computing Systems.
Sensing proposal UCM-UPM
TEC2012-33892.
Datacenter Spanish Ministry of Economy and
Competitiveness
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17. Research goals
Expected impact
• Energy and CO2 carbon footprint reduction in
data centers
– So far, 25% reduction in IT resource management
optimizations
– 10% energy reduction in fan control policies
• Joint IT/cooling techniques are expected to
bring much more benefits.
• Solutions in a real environment: CeSViMa
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18. Questions?
Thank you for
your attention
Marina Zapater
marina@die.upm.es
B105. ETSI Telecomunicación
Avda Complutense, 30
91 549 57 00 (+ 4227)
http://greenlsi.die.upm.es
http://artecs.dacya.ucm.es/
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19. Research results
• M. Zapater, J.L. Ayala, J.M. Moya, K. Vaidyanathan, K. Gross, A.K. Coskun, Leakage and Temperature Aware
Server Control for Improving Energy Efficiency in Data Centers. To apper in: DATE’13, 2013.
• M. Zapater, J. L. Ayala, and J. M. Moya. “GreenDisc: a HW/SW energy optimization framework in globally
distributed computation”, J. Bravo, D. López-de Ipiña, and F. Moya, Ed., Springer Berlin
Heidelberg, 2012, pp. 1-8.
• M. Zapater, J. L. Ayala, and J. M. Moya, “Leveraging heterogeneity for energy minimization in data
centers”, in Proceedings of the 2012 12th IEEE International Symposium on Cluster, Cloud and Grid
Computing (CCGRID 2012), Washington, DC, USA, 2012.
• M. Zapater, C. Sanchez, J. L. Ayala, J. M. Moya, and J. L. Risco-Martín, “Ubiquitous green computing
techniques for high demand applications in smart environments” Sensors, vol. 12, iss. 8, pp. 10659-
10677, 2012
• M. Zapater, P. Arroba, J. M. Moya, and Z. Bankovic, “A State-of-the-Art on energy efficiency in today’s
datacentres: researcher’s contributions and practical approaches”, UPGRADE, vol. 12, iss. 4, pp. 67-
74, 2011.
• M. Zapater, J. L. Risco, J. L. Ayala, and J. M. Moya, “Combined Dynamic-Static approach for Thermal-
Awareness in heterogeneous data centers” IWIA 2010.
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