Ressource allocation and schelduling models for cloud computing

189 Pages·2016·4.98 MB·English

by  Fei Teng

Ressource allocation and schelduling models for cloud
computing
Fei Teng
To cite this version:
Fei Teng. Ressource allocation and schelduling models for cloud computing. Other. Ecole Centrale
Paris, 2011. English. ￿NNT: 2011ECAP0043￿. ￿tel-00659303￿
HAL Id: tel-00659303
https://theses.hal.science/tel-00659303
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E´COLECENTRALEPARIS
ETMANUFACTURES
E´COLECENTRALEPARIS
hh ii
`
THESE
pre´sente´epar
FeiTENG
pourl’obtentiondu
GRADE DE DOCTEUR
Spe´cialite´ : Mathe´matiquesapplique´esetinformatique
Laboratoired’accueil: Laboratoiremathe´matiquesapplique´esauxsyste`mes
SUJET:MANAGEMENTDESDONNE´ESETORDONNANCEMENTDESTAˆCHESSUR
ARCHITECTURESDISTRIBUE´ES
soutenuele: 21Octobre2011
devantunjurycompose´ de:
ChristopheCe´rin Examinateur
GillesFedak Examinateur
TianruiLi Rapporteur
Fre´de´ricMagoule`s Directeurdethe`se
SergePetiton Rapporteur
2011-.......1)
1)Nume´rod’ordrea`demanderauBureaudel’E´coleDoctoraleavantletiragede´finitifdelathe`se.
ii
Abstract
Cloudcomputing,thelong-helddreamofcomputingasautility,hasthepotential
totransformalargepartoftheITindustry, makingsoftwareevenmoreattractive
as a service and shaping the way in which hardware is designed and purchased.
We review the new cloud computing technologies, and indicate the main chal-
lenges fortheirdevelopment infuture, among whichresourcemanagement prob-
lemstandsoutandattractsourattention. Combiningthecurrentschedulingtheo-
ries,weproposecloudschedulinghierarchytodealwithdifferentrequirementsof
cloudservices.
From the theoretical aspect, we mainly accomplish three research issues. Firstly,
we solve the resource allocation problem in the user-level of cloud scheduling.
We propose game theoretical algorithms for user bidding and auctioneer pricing.
With Bayesian learning prediction, resource allocation can reach Nash equilib-
rium among non-cooperative users even though common knowledge is insuffi-
cient. Secondly, we address the task scheduling problem in the system-level of
cloudscheduling. Weproveanewutilizationboundtosettleon-lineschedulabil-
itytestconsideringthesequentialfeatureofMapReduce. Wededucetherelation-
ship between cluster utilization bound and the ratio of Map to Reduce. This new
schedulable bound with segmentation uplifts classical bound which is most used
in industry. Thirdly, we settle the evaluation problem for on-line schedulability
tests in cloud computing. We propose a concept of test reliability to express the
probabilitythatarandomtasksetcouldpassagivenschedulabilitytest. Thelarger
the probability is, the more reliable the test is. From the aspect of system, a test
withhighreliabilitycanguaranteehighsystemutilization.
From the practical aspect, we develop a simulator to model MapReduce frame-
work. ThissimulatoroffersasimulatedenvironmentdirectlyusedbyMapReduce
theoreticalresearchers. TheusersofSimMapReduceonlyconcentrateonspecific
research issues without getting concerned about finer implementation details for
diverse service models, so that they can accelerate study progress of new cloud
technologies.
Keywords: Cloudcomputing,MapReduce,resourceallocation,gametheory,uti-
lizationbound,schedulabilitytest,reliabilityevaluation,simulator
Acknowledgements
Iwouldliketoexpressmygratitudetothosewhohavemadethisthesispossible.
First and foremost, I offer my sincerest gratitude to my supervisor, Professor
Fre´de´ricMagoule`s,whosupportedmethroughoutmythesiswithhispatienceand
guidance whilst allowing me the room to work in my own way. For me, he was
notonlyarespectablescientistwholedmeonthewaytodoresearch,butalsoan
attentivetutorwhotrainedmetobeagoodprocessorinmyfuturecareer. Ireally
appreciateeverythinghehasdoneinthepastthreeyears.
Specialthanksshouldbegiventomyco-supervisor,ProfessorLeiYuwhooffered
me a great help in my research. His professional abilities and knowledge are al-
waysadmired. WhenIencounteredtheobstaclesinresearch, thediscussionwith
him often inspired me, theoretically or practically. I greatly thank him for all the
kindness,supportandencouragement.
Iwouldliketoexpressmysincerethanksmycollegesandfriends,JiePan,Haix-
iang Zhao, Lve Zhang, Ce´deric Venet, Florent Pruvost, Thomas Cadeau, Thu
Huyen Dao, Somanchi K Murthy, Sana Chaabane and Alain Rueyahana in our
team, as well as many other students in Ecole Centrale Paris. They are so con-
siderate, generous, and delightful to be got along with. I had so many pleasant
memoriesaboutthem,sharingusefulinformationtogether,discoveringtheamaz-
ingworldtogether,andlaughingevencryingtogether. Ididenjoythethreeyears
withthem.
I greatly appreciate Sylvie Dervin, Catherine L’hoˆpital and Ge´raldine Carbonel
for helping me settle down and integrate myself into the new environment. They
alwaysencouragedmewithwarmsmiles, andwerereadytosavemewhenIwas
introubles.
Many thanks to my thesis reviewers and the defense jury committee. Professor
Serge Petiton, Professor Tianrui Li, Professor Christophe Ce´rin and Professor
GillesFedak. Theirvaluablecommentsandsuggestionspushedmetogofaraway
ontheroadofscience.
Last but not least, I would like to dedicate this thesis to my parents, families and
the people I love and who love me. Without their continuous supports, I cannot
completethethesisaloneinaforeigncountry.
FeiTeng
2011-10-19
vi
Contents
ListofFigures xiii
ListofTables xv
Glossary xvii
1 Introduction 1
1.1 Researchbackground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Challengesandmotivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Objectivesandcontributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Organizationofdissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Cloudcomputingoverview 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Clouddefinitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Systemarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.3 Deploymentmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Cloudevolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Gettingreadyforcloud . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Abriefhistory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.3 Comparisonwithrelatedtechnologies . . . . . . . . . . . . . . . . . . 13
2.3 Cloudservice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 SoftwareasaService . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 PlatformasaService . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 InfrastructureasaService . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Cloudcharacteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
vii
CONTENTS
2.4.1 Technicalaspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.2 Qualitativeaspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.3 Economicaspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Cloudprojects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5.1 Commercialproducts . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5.2 Researchprojects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5.3 Openareas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Schedulingproblemsforcloudcomputing 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Schedulingproblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.1 Problems,algorithmsandcomplexity . . . . . . . . . . . . . . . . . . 27
3.2.2 Schematicmethodsforschedulingproblem . . . . . . . . . . . . . . . 29
3.3 Schedulinghierarchyinclouddatacenter . . . . . . . . . . . . . . . . . . . . . 32
3.4 Economicmodelsforresource-provisionscheduling. . . . . . . . . . . . . . . 34
3.4.1 Market-basedstrategies . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.2 Auctionstrategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.3 Economicschedulers . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 Heuristicmodelsfortask-executionscheduling . . . . . . . . . . . . . . . . . 41
3.5.1 Staticstrategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5.2 Dynamicstrategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.3 Heuristicschedulers . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.6 Real-timeschedulingforcloudcomputing . . . . . . . . . . . . . . . . . . . . 49
3.6.1 Fixedprioritystrategies . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.6.2 Dynamicprioritystrategies . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6.3 Real-timeschedulers . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Resource-provisionschedulinginclouddatacenter 55
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Gametheory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.1 Normalformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.2 Typesofgames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
viii
CONTENTS
4.2.3 Payoffchoiceandutilityfunction . . . . . . . . . . . . . . . . . . . . 59
4.2.4 StrategychoiceandNashequilibrium . . . . . . . . . . . . . . . . . . 61
4.3 Motivationfromequilibriumallocation . . . . . . . . . . . . . . . . . . . . . 62
4.4 Game-theoreticalallocationmodel . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4.1 Bid-sharedauction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4.2 Non-cooperativegame . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.3 Bidfunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.4.4 Parameterestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4.5 Equilibriumprice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.5 Resourcepricingandallocationalgorithms . . . . . . . . . . . . . . . . . . . 74
4.5.1 Cloudsimtoolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.5.2 Communicationamongentities . . . . . . . . . . . . . . . . . . . . . 75
4.5.3 Implementationalgorithm . . . . . . . . . . . . . . . . . . . . . . . . 77
4.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.6.1 Experimentsetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.6.2 Nashequilibriumallocation . . . . . . . . . . . . . . . . . . . . . . . 79
4.6.3 Comparisonofforecastingmethods . . . . . . . . . . . . . . . . . . . 81
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 Real-timeschedulingwithMapReduceinclouddatacenter 83
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2 Real-timescheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.2.1 Real-timetask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.2.2 Processingresource . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.2.3 Schedulingalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.2.4 Utilizationbound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.3 MotivationfromMapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.4 Real-timeschedulingmodelforMapReducetasks . . . . . . . . . . . . . . . . 91
5.4.1 Systemmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.4.2 BenefitfromMapReducesegmentation . . . . . . . . . . . . . . . . . 92
5.4.3 Worstpatternforschedulabletaskset . . . . . . . . . . . . . . . . . . 93
5.4.4 Generalizedutilizationbound . . . . . . . . . . . . . . . . . . . . . . 96
5.5 Numericalanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
ix