energy-efficient data centers: exploiting knowledge about application and resources

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Energy-efficient data centers: Exploiting knowledge about

application and resources

José M. Moya <[email protected]>Integrated Systems Laboratory

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



José M.Moya | Madrid (Spain), July 27, 2012 2

Data centers




Power distribution




Power distribution (Tier 4)




Contents

• Motivation• Our approach

– Scheduling and resource management

– Virtual machine optimizations

– Centralized management of low-power modes

– Processor design

• Conclusions




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




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

5000

10000

15000

20000

25000

30000

35000

High-end serversMid-range serversVolume servers

Wor

ld se

rver

inst

alle

d ba

se

(tho

usan

ds)

2000 2005 20100

50

100

150

200

250

300

InfrastructureCommunicationsStorageHigh-end serversMid-range serversVolume serversEl

ectr

icity

use

(b

illio

n kW

h/ye

ar)

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




Temperature-dependent reliability problems

Time-dependent dielectric-

breakdown (TDDB)

Electromigration (EM)

Stress migration (SM)

Thermal cycling (TC)

✔ ✖

✖✖



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Cooling a data center





• Virtualization - 27%• Energy Star server

conformance = 6.500

• Better capacity planning 2.500

Server improvements



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Cooling improvements

• Improvements in air flow management and wider temperature ranges


Energy savings up to 25% 25.000Return of investmentin only 2 years




AC DC– 20% reduction 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




Best practices




And… what about IT people?




PUEPower Usage Effectiveness

• 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 is seen in

the electricity bill

• But how can we reduce PIT ?



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Potential energy savings by abstraction level





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




Holistic approach

Chip Server Rack Room Multi-room

Sched & alloc 2 1

app

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5




1. Room-level resource management


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 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)

CCGrid 2012


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



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Current scenario

WORKLOAD Scheduler Resource Manager

Execution




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Potential improvements with best practices




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Cooling-aware scheduling and resource allocation


iMPACT Lab (Arizona State U)



Application-aware scheduling and resource allocation

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LSI-UPM

WORKLOAD

Resource Manager(SLURM)

Execution

Profiling and Classification

Energy Optimization




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• Workload:– 12 tasks from SPEC CPU INT 2006– Random workload composed by 2000 tasks, divided into

job sets– Random job set arrival time

• Servers:

Scenario





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Energy profiling

WORKLOAD


Execution


Energy Optimization

Energy profiling




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Workload characterization





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Optimization

WORKLOAD


Execution


Energy Optimization

Energy Minimization:• Minimization subjected to constraints• MILP problem (solved with CPLEX)• Static and Dynamic





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Static optimization

• Definition of optimal data center– Given a pool of 100 servers of each kind– 1 job set from workload– The optimizer chooses the best selection of servers– Constraints of cost and space

Best solution:• 40 Sparc• 27 AMD

Savings:• 5 a 22% energy• 30% time





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Dynamic optimization

• Optimal workload allocation– Complete workload (2000 tasks)– Good enough resource allocation in terms of energy (not

the best)– Run-time evaluation and optimization

Energy savings ranging from 24% to 47%





• First proof-of-concept regarding the use of heterogeneity to save energy

• Automatic solution• Automatic processor selection offers notable energy

savings• Easy implementation in real scenarios

– SLURM Resource Manager– Realistic workloads and servers

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Conclusions




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2. Server-level resource management



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 "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




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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3. Application-aware and resource-aware virtual

machine



Sched & alloc 2 1

app

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5




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




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




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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4. Global automatic management of low-power

modes



Sched & alloc 2 1

app

OS/middleware

Compiler/VM 3 3

architecture 4 4

technology 5




DVFS – Dynamic Voltage and Frequency Scaling

• As supply 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




Room-level DVFS

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

• There exist optimal algorithms for a known task set (YDS)

• Workload knowledge allows to globally schedule low-power modes without any impact in performance




Parallelism to save energy



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5. Temperature-aware floorplanning of MPSoCs and many-cores



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 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




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




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




Conclusions

• Reducing PUE is not the same as 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 as reducing the electricity bill



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Contact


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

ETSI de Telecomunicación, B104Avenida Complutense, 30Madrid 28040, Spain

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