Thermal aware workload scheduling with backfilling for green data centers

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Thermal Aware Workload

Scheduling with Backﬁlling for Green

Data Centers

Lizhe Wang, Gregor von Laszewski,

Jai Dayal, Thomas R. Furlani

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Outline

• Background and related work

• Models

• Research problem definition

• Scheduling algorithm

• Performance study

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Context

Cyberaide

A project that aims to make advanced cyberinfrastructure

easier to use

Future Grid A newly NSF funded

project to provide a testbed that integrates

the ability of dynamic provisioning of

resources.

(Geoffrey C. Fox is PI)

GreenIT & Cyberaide How do we use

advanced

cyberinfrastructure in an efficient way

GPGPU’s Application use of

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FutureGrid

• The goal of FutureGrid is to support the research that will

invent the future of distributed, grid, and cloud computing.

• FutureGrid will build a robustly managed simulation

environment or testbed to support the development and

early use in science of new technologies at all levels of the

software stack: from networking to middleware to

scientific applications.

• The environment will mimic TeraGrid and/or general

parallel and distributed systems

• This test-bed will enable dramatic advances in science and

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University of Virginia (UV) Technical University Dresden GWT-TUD GmbH, Germany

University of Tennessee – Knoxville (UTK)

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FutureGrid Partners

• Indiana University

• Purdue University

• San Diego Supercomputer Center at University of California San

Diego

• University of Chicago/Argonne National Labs

• University of Florida

• University of Southern California Information Sciences Institute,

University of Tennessee Knoxville

• University of Texas at Austin/Texas Advanced Computing Center

• University of Virginia

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Green computing

• a study and practice of using computing

resources in an efficient manner such that its

impact on the environment is as less

hazardous

as

possible.

–

least amount of hazardous materials are used

–

computing resources are used efficiently in terms

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Cyberaide Project

• A middleware for Clusters, Grids and

Clouds

• A collaboration between IU, RIT, KIT, …

• Project led by

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Objective

• Towards next generation cyberinfrastructure

• Middleware for data centers, grids and clouds

• Environment respect

• To reduce temperatures of computing

resources in a data center, thus reduce

cooling system cost and improve system

reliability

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Model

• Data center

–

Node: <x,y,z>, t

a

_{, Temp(t)}

–

TherMap: Temp(<x,y,z>,t)

• Workload

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t RC-thermal model Online task-temperature Nodei.Temp(t)

Temp(Nodei.<x,y,z>,t)

PR+

Nodei.Temp(0)

task-temperature profile nodei

<x,y,z>

ambient temperature:

TherMap=Temp(Nodei.<x,y,z>,t)

Nodei.Temp(t)

P C R

Nodei.Temp(t)

Temp(Nodei.<x,y,z>,t)

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Research issue definition

• Given a data center, workload, maximum

temperature permitted of the data center

• Min T

response

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Workload model Data center

model

TASA-B

Cooling system control Workload placement

online

task-temperature

input

sche_dule

input input

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task-temperature profile RC-thermal model Workload model Thermal map Data center model TASA-B Cooling system control Workload placement calc ulat

ion task-temperatureonline

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task-temperature profile RC-thermal model Workload model Thermal map Data center model TASA-B Cooling system control Workload placement Cont rol calc ulat

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task-temperature profile RC-thermal model Workload model Thermal map Data center model TASA-B Profiling tool Cooling system control Workload placement Cont rol profiling calc ulat

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task-temperature profile RC-thermal model Workload model Thermal map Data center model TASA-B

Profiling tool monitoring_service

Cooling system control Workload placement Cont rol profiling calc ulat

CFD model provide information Calculate thermal map

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Scheduling framework

Job

subm

ission

Jobs Job queue

Update data center

Information periodically

Job

sc

he

duling

Rack Data center

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Task scheduling algorithm with

backfilling (TASA-B)

• Sort all jobs with decreased order of

task-temperature profile

• Sort all resource with increased order of

predicted temperature

• Hot jobs are allocated to cool resources

• Predict resource temperature based on

online-task temperature

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Node Available

time t0

Time backfilling holes

nodek.tbfsta, backfilling start time of nodek

node

m

ax1

node

m

ax2

nodek.tbfend,

end time for backfilling

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node

m

ax1

Temperature

Tempbfmax

Node Temperature backfilling holes

nodek.Tempbfsta, start temperature for backfilling of nodek

node

m

ax2

nodek.Tempbfend, end

temperature for backfilling

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Simulation

• Data center:

–

Computational Center for Research at UB

–

Dell x86 64 Linux cluster consisting 1056 nodes

–

13 Tflop/s

• Workload:

–

20 Feb 2009 – 22 Mar. 2009

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Simulation result

Metrics TASA

Reduced average temperature 16.1 F Reduced maximum temperature 6.1 F Increase job response time 13.9% Saved power 5000 kW

Reduced CO2 emission 1900kg /hour

Time (hour)

1 ₅₁ ₁₀₁ ₁₅₁ ₂₀₁ ₂₅₁ ₃₀₁ ₃₅₁ ₄₀₁ ₄₅₁ ₅₀₁ ₅₅₁ ₆₀₁ ₆₅₁ ₇₀₁

Average

temperatue

(F

)

70 80 90 100 110

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Simulation result

Metrics TASA-B

Reduced average temperature 14.6 F Reduced maximum temperature 4.1 F Increase job response time 11% Saved power 4000 kW

Reduced CO2 emission 1600kg /hour

1 ₅₁ ₁₀₁ ₁₅₁ ₂₀₁ ₂₅₁ ₃₀₁ ₃₅₁ ₄₀₁ ₄₅₁ ₅₀₁ ₅₅₁ ₆₀₁ ₆₅₁ ₇₀₁