2013-03-11-seminario-artecs-mzapater

Marina Zapater | Seminario ArTeCS 1

MONCLOA Campus of International Excellence

Proactive and Reactive Thermal Optimization Techniques to Improve

Energy Efficiency in Data Centers

Seminario ArTeCSMarina Zapater

Marina Zapater | Seminario ArTeCS

Laboratorio de Sistemas Integrados (LSI)Departamento de Ingeniería ElectrónicaETSI TelecomunicaciónUniversidad Politécnica de Madrid

ArTeCS GroupGroup of Architecture and Technology of Computing SystemsFacultad de InformáticaUniversidad Complutense de Madrid

Presentation

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Data Centers

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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


Motivation

• It is expected for total data center electricity use to exceed 400 GWh/year by 2015.

• The required energy for cooling will continue to be at least as important as the energy required for the computation.

• Energy optimization of future data centers will require a global and multi-disciplinary approach.

2000 2005 20100

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15000

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30000

35000

High-end serversMid-range serversVolume servers

2000 2005 20100

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InfrastructureCommunicationsStorageHigh-end serversMid-range serversVolume servers

5,75 Million new servers per year10% unused servers (CO2 emissions similar to 6,5 million cars)

World server installed base (thousands)

Electricity Use (billion KWh/year)


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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Our perspective

• 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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Proactive and reactive holistic approach


Our perspective

• 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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IT and Cooling power

coolingITTOTAL PPP

IT

TOTAL

P

PPUE


Our perspective

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IT and Cooling power

coolingITTOTAL PPP

IT

TOTAL

P

PPUE

• Minimize IT power• Jointly minimize IT and cooling


Minimizing IT PowerLeveraging 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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Minimizing IT powerApplication Awareness

WORKLOAD Scheduler Resource Manager

Execution

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Minimizing IT PowerApplication Awareness

WORKLOAD Scheduler Resource Manager

Execution

Profiling and Classification

Energy Optimization

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Cooling management

• 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

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Work in collaboration with:

Leakage-cooling tradeoffs at the server level


Cooling management

Work in collaboration with:

Leakage-cooling tradeoffs at the server level

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Cooling & IT Joint Opt.

• 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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Work in Progress


Holistic aproach

• System that increases the knowledge of the data center• Real implementation scenario at CeSViMa• Power optimization in globally distributed systems

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Proactive and reactive techniques

Data Center state Optimization

Datacenter

SensingDecission proposal

GreenDISC Project: HW/SW Technologies for Energy Efficiency in Distributed Computing Systems.UCM-UPM

TEC2012-33892.Spanish Ministry of Economy and Competitiveness


Research goals

• 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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Expected impact


Questions?

Thank you for your attention

Marina [email protected]

B105. ETSI TelecomunicaciónAvda Complutense, 3091 549 57 00 (+ 4227)

http://greenlsi.die.upm.eshttp://artecs.dacya.ucm.es/

http://greenlsi.die.upm.es/

http://artecs.dacya.ucm.es/


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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