Thermal-aware Task Thermal-aware Task Placement in Data Placement in Data

CentersCenters

Qinghui TangQinghui Tang

Sandeep K S GuptaSandeep K S Gupta

Georgios Georgios VarsamopoulosVarsamopoulos

IMPACT LabIMPACT Labhttp://impact.asu.edu/http://impact.asu.edu/

Arizona State Arizona State UniversityUniversity

Growth Trends in data Growth Trends in data centerscenters

►Power density increasesPower density increases Circuit density Circuit density increases by a factor of 3 every 2 yearsincreases by a factor of 3 every 2 years Energy efficiency Energy efficiency increases by a factor of 2 every 2 yearsincreases by a factor of 2 every 2 years Effective power density Effective power density increases by a factor of 1.5 every increases by a factor of 1.5 every

2 years2 years[Keneth Brill: The Invisible Crisis in the Data Center][Keneth Brill: The Invisible Crisis in the Data Center]

►Maintenance/TCO risingMaintenance/TCO rising Data Center TCO doubles every three yearsData Center TCO doubles every three years By 2009, the three-year cost of electricity will exceed the By 2009, the three-year cost of electricity will exceed the

purchase cost of the serverpurchase cost of the server Virtualization/Consolidation is a 1-time/short term solutionVirtualization/Consolidation is a 1-time/short term solution

[Uptime Institute][Uptime Institute]

►Thermal management corresponds to an increasing Thermal management corresponds to an increasing portion of expensesportion of expenses Thermal-aware solutions becoming prominentThermal-aware solutions becoming prominent Increasing need for thermal awarenessIncreasing need for thermal awareness

Related Work (extended Related Work (extended domain)domain)

IC Case/chassis room

firmware

O/S

Application

(middleware)

Dynamic voltage scalingDynamic frequency scalingCircuitry redundancy

Fan speed scaling

CPU Load balancing

Thermal-aware VMThermal-aware

data centerjob scheduling

softwaredimension

physicaldimension

Thermal issues inThermal issues indense computer roomsdense computer rooms

(i.e. Data centers, Computer Clusters, Data warehouses)(i.e. Data centers, Computer Clusters, Data warehouses)

► Heat recirculationHeat recirculation Hot air from the equipment air Hot air from the equipment air

outletsoutlets is fed back to the is fed back to the equipment air equipment air inletsinlets

► Hot spotsHot spots Effect of Heat RecirculationEffect of Heat Recirculation Areas in the data center with Areas in the data center with

alarmingly high temperaturealarmingly high temperature

► ConsequenceConsequence Cooling has to be set very low to Cooling has to be set very low to

have have allall inlet inlet temperatures in temperatures in safe operating rangesafe operating range

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Conceptual overview ofConceptual overview ofthermal-aware task placementthermal-aware task placement

Task placement determinestemperature distribution

Temperature distributiondetermines the equipmentpeak air inlet temperature

Peak air inlet temperaturedetermines upper bound toCRAC temperature setting

CRAC temperature settingdetermines it’s efficiency(Coefficient of Performance)

bottomline

There is a task placement that maximizes cooling efficiency. Find it!

The lower the peak inlet temperaturethe higher the CRAC efficiency

Coefficient of Performance(source: HP)

Prerequisites forPrerequisites forthermal managementthermal management

► Task profilingTask profiling CPU utilization, I/O activity etcCPU utilization, I/O activity etc

► Equipment power profilingEquipment power profiling CPU consumption, disk consumption CPU consumption, disk consumption

etcetc► Heat recirculation modelingHeat recirculation modeling► Task management technologiesTask management technologies

►Need for a comprehensive Need for a comprehensive research frameworkresearch framework

Thermal-awareThermal-awarejob schedulingjob scheduling

On-line job scheduling On-line job scheduling algorithm to minimize peak algorithm to minimize peak air inlet temperature, thus air inlet temperature, thus minimizing the cost of minimizing the cost of cooling.cooling.

Thermal ModelsThermal ModelsTo enable on-line real-time thermal-aware job To enable on-line real-time thermal-aware job schedulingscheduling► fast (analytical, non CFD based)fast (analytical, non CFD based)► non-evasive (machine-learning)non-evasive (machine-learning)

CharacterizationCharacterization

Characterize the power Characterize the power consumption of a given workload consumption of a given workload (CPU, memory, disk etc) on a given (CPU, memory, disk etc) on a given equipmentequipment

Thermal management research Thermal management research frameworkframework

Model the thermal impact of Model the thermal impact of multicore systemsmulticore systems

S en s o r D ataG ath er in g S erv ic e

D a ta C e n te rM o n ito rin g

P er fo rm an c eM o n ito r in g S erv ic e

No n -I n v a s iv eTh e rm a lEv a lu a t io n

F as t T h erm alEv alu atio n S erv ic e

T h erm al/P o w er &P er fo rm an c e C o r r e la tio n

S erv ic e

J o b S c h ed u lin gS erv ic e

C lu s te rM a n a g e m e n t

Po licyEn fo rce m e n t

T h erm al M an ag em en tP o lic y En fo rc em en t

S erv ic e

J o b Q u eu esR es o u rc eQ u eu es

T h erm alC o n tr o l P o lic ies

C o o lin g C o n tro lS erv ic e

Air - f lo w C o n tro lS erv ic e

Fa cilityM a n a g e m e n t

R e s o u rce &S e rv e rM a n a g e m e n t

O S -L ev el S erv ic esP er fo rm an c e

M o n ito r in g

T herm al M anagem ent Infras truc ture& S ervic es fo r D ata C enters

http://impact.asu.edu/

Sandeep GuptaQinghui Tang

Tridib MukherjeeMichael Jonas

Georgios Varsamopoulos

Task ProfilingTask Profilingmeasurements at ASU HPC Data Center measurements at ASU HPC Data Center (one (one

chassis)chassis)

Power Model and ProfilingPower Model and Profiling

► Power Power Consumption Consumption is mainly is mainly affected by affected by the CPU the CPU utilizationutilization

► Power Power consumption consumption is linear to is linear to the CPU the CPU utilizationutilizationPP = a = a UU + + bb

Linear Thermal ModelLinear Thermal Model

► Heat Recirculation Heat Recirculation CoefficientsCoefficients AnalyticalAnalytical Matrix-basedMatrix-based

► Properties of modelProperties of model Granularity at air Granularity at air

inlets inlets (discrete/simplified)(discrete/simplified)

Assumes steadiness Assumes steadiness of air flowof air flow

= + ×

inlettemperatures

supplied airtemperatures

heat distribution

powervector

Tin Tsup D P

N 1 A C

R ecircu la tio n

T su p T in T o u t T A C in

N 2 N 3

1 2 1 3

2 13 1

1 1

Benefit: fast thermal Benefit: fast thermal evaluationevaluation

Give workload Run CFD simulation (days)Extract

temperatures

Give workload Compute vector (seconds)

+×

TinTsupD P

Yieldstemperatur

es

Courtesy: Flometrics

Thermal-awareThermal-awareTask Placement ProblemTask Placement Problem

Given an incoming task, find a task partitioning Given an incoming task, find a task partitioning and placement of subtasks to minimize the and placement of subtasks to minimize the (increase of) peak inlet temperature(increase of) peak inlet temperature

= + ×

inlettemperatures

supplied airtemperatures

heat distribution utilization

vector

Tin Tsup D U

(a+

)

bbbbbbb

XInt AlgorithmXInt AlgorithmApproximation solutionApproximation solution(genetic algorithm)(genetic algorithm)► Take a feasible Take a feasible

solution and perform solution and perform mutations until certain mutations until certain number of iterationsnumber of iterations

PP = a = a UU + + bb

InletTemperature

Contrasted scheduling Contrasted scheduling approachesapproaches

► Uniform Outlet Profile (UOP)Uniform Outlet Profile (UOP) Assigning tasks in a way that tries to Assigning tasks in a way that tries to

achieve uniform outlet temperature achieve uniform outlet temperature distributiondistribution

Assigning more task to nodes with low Assigning more task to nodes with low inlet temperature (water filling process)inlet temperature (water filling process)

► Minimum computing energyMinimum computing energy Assigning tasks in a way that keeps the Assigning tasks in a way that keeps the

number of active (power-on) chassis as number of active (power-on) chassis as few as possiblefew as possible

Server with coolest inlet temperature Server with coolest inlet temperature firstfirst

► Uniform Task (UT)Uniform Task (UT) Assigning all chassis the same amount Assigning all chassis the same amount

of tasks (power consumptions)of tasks (power consumptions) All nodes experience the same power All nodes experience the same power

consumption and temperature riseconsumption and temperature rise

OutletTemperature

Simulated EnvironmentSimulated Environment► Used Flometrics Flovent► Simulated a small scale data

center► physical dimensions

9.6m 8.4m 3.6m► two rows of industry

standard 42U racks arranged► CRAC supply at 8 m3/s► There are 10 racks

each rack is equipped with 5 chassis

► 1000 processors in data center. 232KWatts at full utilization

Performance ResultsPerformance Results► Xint outperforms other algorithmsXint outperforms other algorithms► Data Centers almost never run at 100%Data Centers almost never run at 100%

Plenty of room for benefits!Plenty of room for benefits!

Performance ResultsPerformance Results► Xint outperforms other algorithmsXint outperforms other algorithms► Data Centers almost never run at 100%Data Centers almost never run at 100%

Plenty of room for benefits!Plenty of room for benefits!

Power Vector DistributionPower Vector Distribution

key

Xint contradicts “rule of thumb” placement at bottom

Supply Heat Index (SHI)Supply Heat Index (SHI)

►Supply Heat Index Supply Heat Index Metric developed Metric developed

by HP Labsby HP Labs quantifies the quantifies the

overall heat overall heat recirculation of recirculation of data centerdata center

►Xint consistently Xint consistently has the lowest SHIhas the lowest SHI

ConclusionsConclusions

►Thermal-aware task placement can Thermal-aware task placement can significantly reduce heat recirculationsignificantly reduce heat recirculation XInt performance thrives at around 50% XInt performance thrives at around 50%

CPU utilizationCPU utilization►Not much can be done at 100% utilizationNot much can be done at 100% utilization

Cooling savings can exceed 30%Cooling savings can exceed 30%(in comparison to other schemes)(in comparison to other schemes)

►Cost of operation reduces by 15%Cost of operation reduces by 15%(if initially 1:1 ratio of computing-2-cooling)(if initially 1:1 ratio of computing-2-cooling)

Related Work in ProgressRelated Work in Progress

► Waiving simplifying assumptionsWaiving simplifying assumptions Equipment heterogeneity Equipment heterogeneity [INFOCOM 2008][INFOCOM 2008]

Stochastic task arrivalStochastic task arrival

► Thermal maps thru machine learningThermal maps thru machine learning Automated, non-invasive, cost-effective Automated, non-invasive, cost-effective [GreenCom [GreenCom

2007]2007]

► ImplementationsImplementations Thermal-aware Thermal-aware Moab Moab schedulerscheduler Thermal-aware Thermal-aware SLURMSLURM SiCortexSiCortex product thermal management product thermal management

Algorithm AssumptionsAlgorithm Assumptions

► HPC model in mindHPC model in mind Long-running jobs (finish time is the same Long-running jobs (finish time is the same ——

infinity)infinity)► One-time arrival (starting time is the same)One-time arrival (starting time is the same)► Utilization homogeneityUtilization homogeneity

(same utilization throughout task’s length)(same utilization throughout task’s length)► Non preemptive/movable tasksNon preemptive/movable tasks► Data Center equipment homogeneityData Center equipment homogeneity

power consumptionpower consumption computational capabilitycomputational capability

► Cooling is self-controlledCooling is self-controlled

Thank YouThank You

►Questions?Questions?►Comments?Comments?►Suggestions?Suggestions?

http://impact.asu.edu/

Additional SlidesAdditional Slides

Functional model of Functional model of schedulingscheduling

► Tasks arrive at the data centerTasks arrive at the data center► Scheduler figures out the best placementScheduler figures out the best placement

Placement that has minimal impact on peak Placement that has minimal impact on peak inlet temperaturesinlet temperatures

► Assigns task accordinglyAssigns task accordingly

SchedulerTask

TaskTasks

Architectural ViewArchitectural View

Scheduler(Moab, SLURM)

dispatch

MachineLearning

create/update

provideMonitoringProcesses

ThermalModel

report

control

A simple thermal modelA simple thermal model

► Basic Idea:Basic Idea: We don’t need an We don’t need an

extensive CFD modelextensive CFD model We only need to know the We only need to know the

effect of recirculation at effect of recirculation at specific pointsspecific points

► Express recirculation as Express recirculation as “coefficients”“coefficients”

Cou

rtesy

: Inte

l La

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N1

N2

N3

N4

N5

Recirculation coefficients:Recirculation coefficients:a fast thermal modela fast thermal model

►Reduce/Simplify Reduce/Simplify the “thermal the “thermal map” concept to map” concept to points of interest: points of interest: equipment air equipment air inletsinlets

►Can be computed Can be computed from CFD from CFD models/simulatiomodels/simulationsns

Matrix Aaij: portion of heatexhausted from node ithat directly goes to node j

A

recirculation coefficients

Opportunities & ChallengesOpportunities & Challenges► Data centers don’t run at fulll Data centers don’t run at fulll

unitilizationunitilization Can choose among multiple CPUs Can choose among multiple CPUs

to allocate a jobto allocate a job Different thermal impact per CPUDifferent thermal impact per CPU

► Need for fast thermal Need for fast thermal evaluationevaluation

► Temporal and spatial Temporal and spatial Heterogeneity of Data CentersHeterogeneity of Data Centers

In equipmentIn equipment In workloadIn workload

Thermal issuesThermal issues► Heat recirculationHeat recirculation

Increases as equipment density exceeds Increases as equipment density exceeds cooling capacity as plannedcooling capacity as planned

► Hot spotsHot spots Effect of Heat RecirculationEffect of Heat Recirculation

► Impact:Impact:Cooling has to be set low enoughCooling has to be set low enoughto have to have allall inlet inlet temperatures intemperatures insafe operating rangesafe operating range

Data Center Thermal Data Center Thermal ManagementManagement

Increasing need for thermal awarenessIncreasing need for thermal awareness► Power density increasesPower density increases

Circuit density Circuit density increases by a factor of 3 increases by a factor of 3 every 2 yearsevery 2 years

Energy efficiency Energy efficiency increases by a factor of 2 increases by a factor of 2 every 2 yearsevery 2 years

Effective power density Effective power density increases by a increases by a factor of 1.5 every 2 yearsfactor of 1.5 every 2 years

[Keneth Brill: The Invisible Crisis in the Data Center][Keneth Brill: The Invisible Crisis in the Data Center]► Maintenance/TCO risingMaintenance/TCO rising

Data Center TCO doubles every three yearsData Center TCO doubles every three years By 2009, the three-year cost of electricity By 2009, the three-year cost of electricity

will exceed the purchase cost of the serverwill exceed the purchase cost of the server Virtualization/Consolidation is a 1-time/short Virtualization/Consolidation is a 1-time/short

term solutionterm solution► Thermal management corresponds to an Thermal management corresponds to an

increasing portion of expensesincreasing portion of expenses Thermal-aware solutions becoming Thermal-aware solutions becoming

prominentprominent

IC Case/chassis room

firmware

O/S

Application

(middleware)

Dynamic voltage scalingDynamic frequency scalingCircuitry redundancy

Fan speed scaling

CPU Load balancing

Thermal-aware VM

Data centerjob scheduling

softwaredimension

physicaldimension

Thermal-aware solutionsat various levels

A dynamic thermal-A dynamic thermal-aware control platform aware control platform is necessary for online is necessary for online thermal evaluationthermal evaluation

without thermal-awaremanagement

With thermal-awaremanagement

computation

cooling

$1M

$10M

$100M

year

Scheduling Impacts Cooling Scheduling Impacts Cooling SettingSetting

Inlet temperaturedistributionwithout Cooling

25C

25C

Inlet temperaturedistributionwith Cooling

Scheduling 1

Scheduling 2

Different demands for cooling capacity

Results(1)Results(1)►Recirculation CoefficientsRecirculation Coefficients Consistent with datacenter observationsConsistent with datacenter observations Large values are observed along diagonalLarge values are observed along diagonal Strong recirculation among neighboring servers, or Strong recirculation among neighboring servers, or

between bottom servers and top serversbetween bottom servers and top servers

1

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3

4

5

6

7

8

9

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