Thermal-aware Task Thermal-aware Task Placement in Data CentersPlacement in Data Centers

Qinghui TangQinghui Tang

Sandeep K S GuptaSandeep K S Gupta

Georgios VarsamopoulosGeorgios VarsamopoulosIMPACT Lab

http://impact.asu.edu/

Arizona State University

Growth Trends in data centersGrowth Trends in data centers►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 2 yearsincreases by a factor 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 will exceed the purchase By 2009, the three-year cost of electricity will exceed the purchase

cost of the servercost 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 portion Thermal management corresponds to an increasing portion of expensesof expenses Thermal-aware solutions becoming prominentThermal-aware solutions becoming prominent Increasing need for thermal awarenessIncreasing need for thermal awareness

Related Work (extended domain)Related Work (extended 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 recirculation Hot air from the equipment air outlets

is fed back to the equipment air inlets► Hot spots

Effect of Heat Recirculation Areas in the data center with

alarmingly high temperature

► Consequence Cooling has to be set very low to have

all inlet temperatures in safe operating range

Courtesy: Intel Labs

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 etcCPU consumption, disk consumption etc

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

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

Thermal-awarejob scheduling

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

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

CharacterizationCharacterize the power consumption of a given workload (CPU, memory, disk etc) on a given equipment

Thermal management research framework

Model the thermal impact of multicore systems

S e n s o r D a t aG a t h e r i n g S e r v i c e

D a t a C e n t e rM o n i t o r i n g

P e r f o r m a n c eM o n i t o r i n g S e r v i c e

N o n - I n v a s i v eT h e r m a lE v a l u a t i o n

F a s t T h e r m a lE v a l u a t i o n S e r v i c e

T h e r m a l / P o w e r &P e r f o r m a n c e C o r r e l a t i o n

S e r v i c e

J o b S c h e d u l i n gS e r v i c e

C l u s t e rM a n a g e m e n t

P o l i c yE n f o r c e m e n t

T h e r m a l M a n a g e m e n tP o l i c y E n f o r c e m e n t

S e r v i c e

J o b Q u e u e sR e s o u r c eQ u e u e s

T h e r m a lC o n t r o l P o l i c i e s

C o o l i n g C o n t r o lS e r v i c e

A i r - f l o w C o n t r o lS e r v i c e

F a c i l i t yM a n a g e m e n t

R e s o u r c e &S e r v e rM a n a g e m e n t

O S - L e v e l S e r v i c e sP e r f o r m a n c e

M o n i t o r i n g

T h e r m a l M a n a g e m e n t I n f r a s t r u c t u r e& S e r v i c e s f o r D a t a C e n t e r s

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 chassis)(one chassis)

Power Model and ProfilingPower Model and Profiling

► Power Power Consumption Consumption is mainly is mainly affected by the affected by the CPU utilizationCPU utilization

► Power Power consumption is consumption is linear to the linear to the CPU utilizationCPU utilization

PP = a = a UU + b + b

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 e c i r c u l a t i o n

T s u p T i n T o u t T A C i n

N 2 N 3

α 1 2 α 1 3

α 2 1α 3 1

α 1 1

Benefit: fast thermal evaluationBenefit: fast thermal evaluationGive workload Run CFD simulation (days)

Extracttemperatures

Give workload Compute vector (seconds)

+×

TinTsupD P

Yieldstemperatures

Courtesy: Flometrics

Thermal-awareThermal-awareTask Placement ProblemTask Placement Problem

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

= + ×

inlettemperatures

supplied airtemperatures

heat distributionutilization

vector

Tin Tsup D U

(a + )

bbbbbbb

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

and perform mutations until certain number of iterations

PP = a = a UU + b + b

InletTemperature

Contrasted scheduling approachesContrasted scheduling approaches► 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 firstServer with coolest inlet temperature first► Uniform Task (UT)Uniform Task (UT)

Assigning all chassis the same amount of Assigning all chassis the same amount of tasks (power consumptions)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% CPU XInt performance thrives at around 50% CPU

utilizationutilization►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 2007][GreenCom 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 schedulingFunctional model of scheduling

► 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 inlet Placement that has minimal impact on peak inlet temperaturestemperatures

► 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 extensive We don’t need an extensive

CFD modelCFD 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”

Courtesy: Intel Labs

N1

N2

N3

N4

N5

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

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

► Can be computed Can be computed from CFD from CFD models/simulationsmodels/simulations

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

A

recirculation coefficients

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

unitilization Can choose among multiple CPUs

to allocate a job Different thermal impact per CPU

► Need for fast thermal evaluation► Temporal and spatial

Heterogeneity of Data Centers In equipment In workload

Thermal issues► Heat recirculation

Increases as equipment density exceeds cooling capacity as planned

► Hot spots Effect of Heat Recirculation

► Impact:Cooling has to be set low enoughto have all inlet temperatures insafe operating range

Data Center Thermal ManagementData Center Thermal ManagementIncreasing need for thermal awarenessIncreasing need for thermal awareness

► Power density increasesPower density increases Circuit density Circuit density increases by a factor of 3 every increases by a factor of 3 every

2 years2 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 factor increases by a factor

of 1.5 every 2 yearsof 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 will By 2009, the three-year cost of electricity will

exceed the purchase cost of the serverexceed the purchase cost of the server Virtualization/Consolidation is a 1-time/short term Virtualization/Consolidation is a 1-time/short term

solutionsolution► Thermal management corresponds to an Thermal management corresponds to an

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

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 SettingScheduling Impacts Cooling SettingInlet temperaturedistributionwithout Cooling

25°C

25°C

Inlet temperaturedistributionwith Cooling

Scheduling 1

Scheduling 2

Different demands for cooling capacity

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

bottom servers and top servers

1

2

3

4

5

6

7

8

9

10