1IMPACTA r izo n a S tate U n iv e r s ity

Thermal Management of Datacenter

Qinghui Tang

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Preliminaries

What is data center What is thermal management Why does Intel Care Why Computer Science

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Typical layout of a datacenter

Rack outlet temperature Tout

Rack inlet temperature Tin

Air conditioner supply temperature Ts

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State-of-Art Thermal Management of Data Center

Power densities are increasing exponentially along with Moore’s Law

Current cooling solutions at various levels

Chip / component level Server/board level Rack level Data center level

S/W based Thermal management solutions – HP+Duke

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Thermal Management of Datacenter

Motivation and significance Compute Intensive Applications (Online Gaming,

Computer Movie Animation, Data Mining) requiring increased utilization of Data Center

Maximizing computing capacity is a demanding requirement

New blade servers can be packed more densely Energy cost is rising dramatically

Goal Improving thermal performance Lowering hardware failure rate Reducing energy cost

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Typical layout of a datacenter

System Variables Symbol

Inle t air te m p e rature o fse rve rs

T i_ in

O utle t air te m pe rature o fse rve

T i_ o u t

P o we r co nsum p tio n o fse rve rs

P i

He at R e m o val C ap ac ity o fHV A C

H i

P o we r consum p tio n o f airco nd itio ne r

C P i

He at d iss ip atio n rate V i

T e m pe rature thresho ld o fse rve rs

T H i

Applic ation Profile on servers C i

T e m pe rature thresho ld o fe nvironm e nt

T HE

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New Challenges

Planning perspective: How to design efficient data center?

does upgrading 10% blade servers to smart ones help to reduce cost

Operation perspective: How to efficiently operate data center and lower the cost?

What’s the trade-off between utility cost and hardware failure cost

Overcooling: wastes energy and increases utility cost Undercooling: increases frequency of hardware failures

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Research Issues of Thermal Management of Datacenter

Abstract HeatFlow Model

Power & LoadCharacterization

Modeling Thermal Performance

Multiscale & Multimodal Info

Analysis

ThermalPerformanceEvaluation

CostOptimization

SchedulerOther Impact

Factors

Understanding

Control

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Example of multiple granularity and scale

0

20

40

60

80

100

100

100

100

74

76

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82

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88

90

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92

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0

500

1000

1500

2000

2500

3000

3500

2300

2300

2300

Tas k M ap P o we r M ap Te m pe rature M ap

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Multiscale and multimodal nature of datacenter management

Information perspective Multiple system variables Different change pattern Different sampling Rate

Control perspective Responsiveness Control granularity (spatial

and temporal level) Sensitivity Analysis

R o o m lev e l

R o w lev e l

R o o m lev e l

R o w lev e l

R o o m lev e l

T em p o r a lS c a le

S p atia l S c a le

S e co n d s

Minu tes

H ou rs

C h as s is lev e l

R o w lev e l

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Approaches

CFD simulation to characterize thermal performance of data center

Online measurement and feedback control system

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CFD Simulation

CFD real model based on ASU HPC center

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Thermal-aware task scheduling

Se ns o r D ataD atabas e

C FD s im ulat io ns o f tware

P o lic yC o ntro l le rSc he dule r

O the r Im pac tfac to r s

C o lle c t ing e nviro nm e ntal data andlo ad info rm atio n f ro m s e ns o rs

`

C o rre lat io n o flo ad & po we r

C o s t Analys is

Sc he duling P o l ic y

C o ntro l P o l ic y

Inc o m ing tas k

O ns ite s urve y

M a p loa d to pow e rc ons um ption

H is to ry Se ns o r D ata

C ur re nt Se ns o r D ata

S ch em atic V iew o f T h erm al M an agem en t

D atac enter

Abs trac t H e atM o de l1

2

3

4

56

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Two-Pronged Approach

Real-time measurement

Online lightweight simulation & prediction

C F D s im u la tio n o f C o m p u ter R o o m

C o m p u ter R o o m w / s en s o r s

S en s o r D ata

O p er a tio nC o m m an d

I m p ac t o nT em p er a tu r e

C o n tr o lD ec is io n

C R AC u n it

S im u la tio n

T em p er a tu r e

O p er a tio nC o m m an d

I m p ac t o nT em p er a tu r e

O n lin e m o n ito rin g & c o n tro l

C o n tr o lAlg o r ith m s

M o d el o fC R AC u n it

D es ign G uid line toevaluate d ep lo ym entp erfo rm anc e

F eed b ac k to tune ups im ulatio n p aram eters

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Goal: Datacenter energy cost optimization

D is tr ib u tedS er v er m o d el

C o o lin g s y s temm o d el C o o lin g

en er g y c o s t

T h r o u g h p u t o rC o m p u ta tio n C ap ac ity

T h er m al d is tr ib u tio n

T o ta l c o s t

C o m p u ta tio nen er g y c o s t

Har d w ar e c o s t

O p er a tio n c o s t

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Different optimization goals

Maximizing computation capacity given energy cost constraint

Minimizing individual cost (computing cost/cooling cost)

Achieving thermal balancing

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Questions and answers