Proactive Power and Thermal Aware Optimizations for Energy-Efficient Cloud Computing

161 Pages·2017·1.98 MB·Spanish

UNIVERSIDAD POLITE´CNICA DE MADRID
ESCUELA TE´CNICA SUPERIOR
DE INGENIEROS DE TELECOMUNICACIO´N
TESIS DOCTORAL
Proactive Power and Thermal Aware Optimizations
for Energy-Efficient Cloud Computing
Autor:
Patricia Arroba Garc´ıa
IngenierodeTelecomunicacio´n
Directores:
Jose´ Manuel Moya Ferna´ndez
DoctorIngenierodeTelecomunicacio´n
Jose´ Luis Ayala Rodrigo
DoctorIngenierodeTelecomunicacio´n
External Mentor:
Rajkumar Buyya
DoctorofPhilosophyinComputerScienceandSoftwareEngineering
2017
PatriciaArrobaGarc´ıa
E-mail: [email protected]
©2017PatriciaArrobaGarc´ıa
Permissionisgrantedtocopy,distributeand/ormodifythisdocumentunderthetermsoftheGNU
FreeDocumentationLicense,Version1.2oranylaterversio´npublishedbytheFreeSoftware
Foundation;withnoInvariantSections,noFront-CoverTexts,andnoBack-CoverTexts. Acopyofthe
licenseisincludedinthesectionentitledGNUFreeDocumentationLicense.
Ph.D. Thesis
T´ıtulo: ProactivePowerandThermalAwareOptimizations
ForEnergy-EfficientCloudComputing
Autor: PATRICIA ARROBA GARC´IA
Tutor: JOSE´ MANUEL MOYA FERNA´NDEZ
JOSE´ LUIS AYALA RODRIGO
Externalmentor: RAJKUMAR BUYYA
Departamento: DEPARTAMENTO DE INGENIER´IA ELECTRO´NICA
Miembros del tribunal:
Presidente:
Secretario:
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Suplente:
Suplente:
Losmiembrosdeltribunalarribanombradosacuerdanotorgar
lacalificacio´nde:
Madrid, de de2017

“Do.Ordonot.Thereisnotry.”
—Yoda,TheEmpireStrikesBack.Starwars

Acknowledgment
“You’llstaywithme?–’Untiltheveryend’.”
—J.K.Rowling,HarryPotterandtheDeathlyHallows
Haceunosan˜os, cuandodecid´ıdedicarmitrabajoalainvestigacio´n, aposte´ porunal´ıneaemergente,
que se sal´ıa de lo establecido en ese momento. Desde entonces he dedicado mi esfuerzo a una
investigacio´nenlaquecreo,porquepiensoquetieneunobjetivoimportanteanivelsocialyquetiene
ungranimpactoanivelglobal.Yesunadecisio´nquemehahechomuyfeliz.
Misdirectoresdetesissonlosprincipalesresponsablesdesacaradelanteestal´ıneadeinvestigacio´n.
Graciasporsertanvalientes,porapostarporloquecree´isyporapoyarmesiempreanivelprofesionaly
anivelpersonal. AJosem,porserunvisionario,pensarsiemprealogrande,yserelma´soptimista. A
Jose,porquesiempremeimpresionantusconocimientostanmultidisciplinares,porsertaninteligentey
aportarmetantaserenidad.Soisexcepcionales.
Durantemiinvestigacio´nhetenidolasuertedetrabajarcondosgruposcongentemaravillosa. A
mis compan˜eros de GreenLSI, me encanta trabajar con vosotros, juntos somos un gran equipo. A los
investigadoresdeArTeCS,porquerodearsedegentequeesmuch´ısimomejorquetu´ hacequepuedas
crecer. A Marina, por los deadlines, por los cafe´s y por las llamadas Boston-Melbourne y
Laussane-Tokyo.Graciasatodosporensen˜arme,mequedamuchoqueaprenderdevosotros.
I would like to express my sincere gratitude to my mentor Prof. Rajkumar Buyya, from the
University of Melbourne, for his continuous support and immense knowledge. I’d like to thank my
sensei Prof. Satoshi Matsuoka for his advice and support during my research stay at his group at the
TokyoInstituteofTechnology. ThanksalsotothepeopleoftheCLOUDSLab. andtheMatsuokaLab. I
reallyappreciateyourguidanceandgenerosity.
Amisamigos,misJamelgasquesiempreesta´nah´ı,aSerranoyAliciaporacordarsedem´ıaunque
este´bienlejos,aAleporqueavecescreestu´ ma´senm´ıqueyo,yporqueteneroscercaycompartircosas
convosotrosmehacemuyfeliz.
Graciasatodamisuperfamilia,porquecelebrarlosbuenosmomentosconvosotrosesloquemerece
lapena. Papa´,mama´,Edu,siemprenoshemoscuidadoyapoyadoentodoyestonoibaasermenos.
Siemprenoshabe´isensen˜adoqueelesfuerzoylaconstanciatienensurecompensa, ye´staesunama´s
quedisfrutarjuntos.Osquiero!
Y sobre todo a David, por estar ah´ı todos los d´ıas, en Australia, en Japo´n, en Madrid, y en todas
partes.Teadmiroytequiero.Juntosmejor.Siempre.
“Tengounmillo´ndecosasqueagradecerte,perotambie´ntoda
unavidaparahacerlo.”
—Dr.D.Fraga,Trust-ware:AMethodologytoAnalyze,
Design,andSecureTrustandReputationSystems
I

Abstract
“Hey,mamma,Lookatme,
I’monthewaytothepromisedland”
—AC/DC,Highwaytohell
Cloud computing addresses the problem of costly computing infrastructures by providing
elasticitytothedynamicresourceprovisioningonapay-as-you-gobasis,andnowadaysitis
considered as a valid alternative to owned high performance computing clusters. There are
twomainappealingincentivesforthisemergingparadigm: first, utility-basedusagemodels
provided by Clouds allow clients to pay per use, increasing the user satisfaction; then, there
is only a relatively low investment required for the remote devices that access the Cloud
resources[1].
ComputationaldemandondatacentersisincreasingduetogrowingpopularityofCloud
applications. However, these facilities are becoming unsustainable in terms of power
consumptionandgrowingenergycosts. Nowadays,thedatacenterindustryconsumesabout
2% of the worldwide energy production [2]. Also, the proliferation of urban data centers is
responsible for the increasing power demand of up to 70% in metropolitan areas where the
power density is becoming too high for the power grid [3]. In two or three years, this
situation will cause outages in the 95% of urban data centers incurring in annual costs of
about US$2 million per infrastructure [4]. Besides the economical impact, the heat and the
carbonfootprintgeneratedbycoolingsystemsindatacentersaredramaticallyincreasingand
theyareexpectedtoovertakeairlineindustryemissionsby2020[5].
TheCloudmodelishelpingtomitigatethisissue,reducingcarbonfootprintperexecuted
task and diminishing CO emissions [6], by increasing data centers overall utilization.
2
According to the Schneider Electric‘s report on virtualization and Cloud computing
efficiency [7], Cloud computing offers around 17% reduction in energy consumption by
sharingcomputingresourcesamongallusers. However,Cloudprovidersneedtoimplement
an energy-efficient management of physical resources to meet the growing demand of their
serviceswhileensuringsustainability.
The main sources of energy consumption in data centers are due to computational
Information Technology (IT) and cooling infrastructures. IT represents around 60% of the
total consumption, where the static power dissipation of idle servers is the dominant
contribution. On the other hand, the cooling infrastructure originates around 30% of the
overallconsumptiontoensurethereliabilityofthecomputationalinfrastructure[8]. Thekey
factor that affects cooling requirements is the maximum temperature reached on the servers
duetotheiractivity,dependingonbothroomtemperatureandworkloadallocation.
Static consumption of servers represents about 70% of the IT power [9]. This issue is
intensified by the exponential influence of temperature on the leakage currents. Leakage
powerisacomponentofthetotalpowerconsumptionindatacentersthatisnottraditionally
considered in the set point temperature of the room. However, the effect of this power
contribution, increased with temperature, can determine the savings associated with the
proactivemanagementofthecoolingsystem. Oneofthemajorchallengestounderstandthe
thermal influence on static energy at the data center scope consists in the description of the
trade-offsbetweenleakageandcoolingconsumption.
III
The Cloud model is helping to reduce the static consumption from two perspectives
based on VM allocation and consolidation. First, power-aware policies reduce the static
consumption by increasing overall utilization, so the operating server set can be reduced.
Dynamic Voltage and Frequency Scaling (DVFS) is applied for power capping, lowering
servers’energyconsumption. Then, thermal-awarestrategieshelptoreducehotspotsinthe
IT infrastructure by spreading the workload, so the set point room temperature can be
increased resulting in cooling savings. Both thermal and power approaches have the
potentialtoimproveenergyefficiencyinCloudfacilities. Unfortunately,thesepoliciesarenot
jointly applied due to the lack of models that include parameters from both power and
thermal approaches. Deriving fast and accurate power models that incorporate these
characteristics, targeting high-end servers, would allow us to combine power and
temperaturetogetherinanenergyefficientmanagement.
Furthermore, as Cloud applications expect services to be delivered as per Service Level
Agreement (SLA), power consumption in data centers has to be minimized while meeting
this requirement whenever it is feasible. Also, as opposed to HPC, Cloud workloads vary
significantlyovertime, makingoptimalallocationandDVFSconfigurationnotatrivialtask.
AmajorchallengetoguaranteeQoSforCloudapplicationsconsistsinanalyzingthetrade-offs
betweenconsolidationandperformancethathelptocombineDVFSwithpowerandthermal
strategies.
The main objective of this Ph.D. thesis is to address the energy challenge in Cloud data
centersfromathermalandpower-awareperspectiveusingproactivestrategies. Ourresearch
proposes the design and implementation of models and global optimizations that jointly
consider energy consumption of both computing and cooling resources while maintaining
QoSfromanewholisticperspective.
ThesisContributions: Tosupportthethesisthatourresearchcandeliversignificantvalue
intheareaofCloudenergy-efficiency,comparedtotraditionalapproaches,wehave:
• Definedataxonomyonenergyefficiencythatcompilesthedifferentlevelsofabstraction
thatcanbefoundindatacentersarea.
• Classified state-of-the-art approaches according to the proposed taxonomy, identifying
newopenchallengesfromaholisticperspective.
• Identified the trade-offs between leakage and cooling consumption based on empirical
research.
• Proposednovelmodelingtechniquesfortheautomaticidentificationoffastandaccurate
models,providingtestinginarealenvironment.
• AnalyzedDVFS,performanceandpowertrade-offsintheCloudenvironment.
• Designed and implemented a novel proactive optimization policy for dynamic
consolidationofvirtualmachinesthatcombineDVFSandpower-awarestrategieswhile
ensuringQoS.
• DerivedthermalmodelsforCPUandmemorydevicesvalidatedinrealenvironment.
• DesignedandimplementednewproactiveapproachesthatincludeDVFS,thermaland
power considerations in both cooling and IT consumption from a novel holistic
perspective.
• Validatedouroptimizationstrategiesinsimulationenvironment.
IV