energy efficiency in data centers

Energy Efficiency in Data Centers

Marina Zapater

Marina Zapater | Going Green1

GreenLSI – Integrated Systems LabElectronic Engineering Dept

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Marina Zapater | Going Green 2

Data Centers

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Outline

• Why Data Centers (DC) in this Workshop?

• The DC in next-generation applications

• Energy consumption at the Data Center

• Insight on optimization strategies

• Conclusions

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Outline





• Conclusions

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US EPA 2007 Report to Congress on Server and Data Center Energy Efficiency

Why DC in this Workshop?Motivation

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Motivation

• Energy consumption of data centers– 1.3% of worldwide energy production 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 consumption than many industries (paper, automotive, petrol, wood, or plastic)

Jonathan Koomey. 2011. Growth in Data center electricity use 2005 to 2010

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Motivation

José M.Moya | Madrid (Spain), July 27, 2012 7

• 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.

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

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What about urban DC?

• 50% of urban DC have already or will soon reach the maximum capacity of the power grid

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Tier 4 Data Center

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Outline





• Our vision and future trends

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The DC in next generation applications

• Traditional uses of Data Centers:– Webmail, Web search, Databases, Social networking or distributed storage, High-

performance computing (HPC), Cloud computing

• Next-generation applications:– Population monitoring applications: e-Health, Ambient Assisted Living– Smart cities

• Next-generation applications generate huge amounts of data• Need to store, analize and generate knowledge

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Global energy optimization

• Solution: GoingGreen! • How: Global energy optimization strategies

– Proposal of a holistic energy optimization framework– Minimizing overall power consumption– Multi-level optimization: WBSN, Personal Servers and Data Centers

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Global energy optimization

• Executing part of the workload in the Personal Servers – Classifying tasks depending on their demand– Resource management techniques based on fast runtime allocation

algorithms executed on the Personal Servers– Executing some tasks in Personal Servers instead of forwarding load to DC.– Up to 10% in energy savings and 15% execution time savings

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Outline





• Conclusions

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Energy Consumption at the DCWhat is really a Data Center?

http://cesvima.upm.es

WORKLOAD Scheduler Resource Manager

Execution

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Energy Consumption at the DCHow does cooling work?

• Typical raised-floor air-cooled Data Center:

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Energy Consumption at the DCPower consumption breakdown

• The major contributors to electricity costs are:– Cooling (around 50%)– Servers (around 30-40%)

• The most common metric to measure efficiency in Data Centers is PUE (Power Usage Effectiveness)

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Power Usage Effectiveness (PUE)

• Average PUE ≈ 2• State of the Art: PUE ≈ 1,2– The important part is IT energy consumption– Current work in energy efficient data centers is focused in

decreasing PUE– Decreasing PIT does not decrease PUE, but it has in impact

on the electricity bill

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“Traditional” approachesWhat would Google do?

PUE = 1.2

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Research trends

Abstraction level

• Higher levels of abstraction bring more benefits

• Some areas have brought more benefits than others

Solutions proposed by the State of the Art

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Outline





• Conclusions

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Our approach

• Global strategy to allow the use of multiple information sources to coordinate decisions in order to reduce the total energy consumption

• Use of knowledge about the energy demand characteristics of the applications, and characteristics of computing and cooling resources to implement proactive optimization techniques

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Energy Optimization:Holistic Approach

Chip Server Rack Room Multi-room

Sched & alloc 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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Resource Management at the Room level


Sched & alloc 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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Resource Management at the Room levelLeveraging heterogeneity – IT perspective

• Use heterogeneity to minimize energy consumption from a static/dynamic point of view– Static: Finding the best data center set-up, given a number of

heterogeneous machines– Dynamic: Optimization of task allocation in the Resource Manager

• We show that the best solution implies an heterogeneous data center– Most data centers are heterogeneous (several generations of

computers)– 5 to 22% energy savings for static solution– 24% to 47% energy savings for dynamic solution

M. Zapater, J.M. Moya, J.L. Ayala. Leveraging Heterogeneity for Energy Minimization in Data Centers, CCGrid 2012

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Resource Management at the Room levelLeveraging heterogeneity – IT perspective

• Energy profiling of tasks of the SPEC CPU 2006 benchmark• Usage of MILP algorithms to schedule tasks in servers where

they consume less energy• Implemented in a real resource manager (SLURM)

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Resource Management at the Room level

IT + Cooling perspective

• Generating a thermal model for the data room:– Data Center environmental

monitoring to gather temperature, humidity, differential pressure

– Predict server temperature and room temperature

• Optimum resource management attending to cooling and IT power– Real environment with

heterogeneous servers– SLURM resource manager

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Resource Management at the Server level


Sched & alloc 2 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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Resource Management at the Server levelLeakage-temperature tradeoffs - Cooling

• Exploring the leakage-temperature tradeoffs at the server level– At higher temperatures, CPU increases power consumption due to

leakage– To decrease CPU temperature, fan speed raises, increasing server

cooling consumption.

M. Zapater, J.L. Ayala., J.M. Moya, K. Vaidyanathan, K. Gross, and A. K. Coskun, “Leakage and temperature aware server control for improving energy efficiency in data centers”, DATE 2013

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Resource Management at the Server levelLeakage-temperature tradeoffs - Cooling

• Implemented fan speed controllers that reduce server power consumption by 10%.

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Resource Management at the Chip level


Sched & alloc 2 2 1

Application

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 "Static and dynamic temperature-aware scheduling for multiprocessor SoCs", IEEE Trans. Very Large Scale Integr. Syst., vol. 16, no. 9, pp.1127 -1140 2008

UCSD – System Energy Efficiency Lab

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Scheduling and resource allocation policies in MPSoCs

• Energy characterization of applications allows to define proactive scheduling and resource allocation policies, minimizing hotspots

• Hotspot reduction allows to raise cooling temperature

+1oC means around 7% cooling energy savings

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Energy Optimization:Holistic Approach


Sched & alloc 2 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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JIT Compilation in Virtual Machines

• Virtual machines compile (JIT compilation) the applications into native code for performance reasons

• The optimizer is general-purpose and focused in performance optimization

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Back-end

JIT compilation for energy minimization

• Application-aware compiler– Energy characterization of applications and transformations– Application-dependent optimizer– Global view of the data center workload

• Energy optimizer– Currently, compilers for high-end processors oriented to performance

optimization

Front-end Optimizer 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 optimization and profiling of energy consumption in embedded systems,” International Symposium on System Synthesis, pages 193 – 199, Sept. 2000

– 77% energy reduction in MP3 decoder

Fei, Y., Ravi, S., Raghunathan, A., and Jha, N. K. 2004. Energy-optimizing source code transformations for OS-driven embedded software. In Proceedings of the International Conference VLSI Design. 261–266.

– Up to 37,9% (mean 23,8%) energy savings in multiprocess applications running on Linux

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Global Management of Low Power Modes


Sched & alloc 2 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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Global Management of Low-power modes (DVFS)

• DVFS (Dynamic Voltage and Frequency Scaling) is based upon:– As suppy voltage decreases, power decreases quadratically– But delay increases (performance decreases) only linearly– The maximum frequency also decreases linearly

• Currently, low-power modes, if used, are activated by inactivity of the server operating system

• To minimize energy consumption, changes between modes should be minimized

• On the other hand, workload knowledge allows to globally schedule low-power modes without any impact in performance

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Global Management of Low-power modes (DVFS)

• By using a thermal model, we can predict the behaviour of a workload under each power mode

• We can use resource management algorithms to change DVFS on runtime, adapting to our workload.

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Temperature-aware floorplanning of MPSoCs


Sched & alloc 2 2 1

Application

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5

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Temperature-aware floorplanning of MPSoCs

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Potential energy savings with floorplanning

– Up to 21oC reduction of maximum temperature– Mean: -12oC in maximum temperature– Better results in the most critical examples

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 getting 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 reduction per layer in a 3D chip with 4 layers and 48 cores

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Outline





• Conclusions

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There is still much more to be done

• Smart Grids– Consume energy when everybody else does not– Decrease energy consumption when everybody

else is consuming• Reducing the electricity bill– Variable electricity rates– Reactive power coefficient– Peak energy demand

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Conclusions

• Reducing PUE is not the same than reducing energy consumption– IT energy consumption dominates in state-of-the-art data centers

• Application and resources knowledge can be effectively used to define proactive policies to reduce the total energy consumption– At different levels– In different scopes– Taking into account cooling and computation at the same time

• Proper management of the knowledge of the data center thermal behavior can reduce reliability issues

• Reducing energy consumption is not the same than reducing the electricity bill

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Thank you for your attention

Marina [email protected]://greenlsi.die.upm.es(+34) 91 549 57 00 x-4227ETSI de Telecomunicación, B105Avenida Complutense, 30Madrid 28040, Spain

Thanks to our collaborators:

energy efficiency in data centers

Technology

greenmarina zapater

data centers dc

data center electricity

urban dc

worldwide energy production

required energy forcooling

thedata center insight

huge amounts of data