Resource allocation in hard real-time avionic systems. Scheduling and routing problems

161 Pages·2017·3.37 MB·English

by  Ahmad Al Sheikh

Resource allocation in hard real-time avionic systems.
Scheduling and routing problems
Ahmad Al Sheikh
To cite this version:
Ahmad Al Sheikh. Resource allocation in hard real-time avionic systems. Scheduling and routing
problems. Automatique / Robotique. INSA de Toulouse, 2011. Français. ￿NNT: ￿. ￿tel-00631443￿
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InstitutNationaldesSciencesAppliquéesdeToulouse(INSAToulouse)
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SystèmesInformatiquesetSystèmesEmbarqués
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AhmadA(cid:0)LSHEIKH
(cid:76)(cid:69)(cid:0)(cid:26) 2 8Septembre2011 (cid:0)
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Resourceallocationinhardreal-timeavionicsystems
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Schedulingandroutingproblems
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Système(cid:0)s(EDSYS)
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LAAS-CNRS
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OlivierBRUN(LAAS-CNRS)
Pierre-EmmanuelHLADIK(LAAS-CNRS)
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SanjoyBARUAH(UniversityofNorthCarolina)
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YvesSOREL(INRIA)
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FrédéricBONIOL(ONERA)
JoëlGOOSSENS(UniversitéLibredeBruxelles)
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Acknowledgements
First and foremost, my appreciation goes to my doctoral advisors, Dr. Olivier Brun and Dr. Pierre-
Emmanuel Hladik for their support throughout my PhD. They have successfully provided me with a
fulfilling and motivating work environment in which I have conducted my research. I thank them for
thisuniqueexperience forwhichIamdeeplygrateful.
IwouldalsoliketooffermyappreciationtoDr. BalakrishnaPrabhuforhissupportandcontribution
inmanyoccasions. Hisintervention hadagreatimpactonthedirection thisthesishastook.
IwouldliketothanktheJuryforhavingacceptedtoparticipateinmyPhDdefense. Beginningwith
Professor SanjoyBaruahandProfessor YvesSorelwhohaveconsecrated muchoftheirtimetoreview
mydissertationandgivedetailedfeedbackonmywork,toProfessorFre´de´ricBoniolandProfessorJoe¨l
Goossens whoparticipated inexamining myworkandproviding valuable insights totheimprovement
ofthequality ofthisthesis.
I would like to acknowledge all the participants in the research project SATRIMMAPfor the sup-
porttheyhavegivenmethroughout the3yearsofthePhD.Thissupportwasofutmostimportanceand
wasessential totheaccomplishment oftheobjectives setbeforeus.
Iamextremely grateful forthematesIgottoknowandshare officeswithintheLaboratory ofAr-
chitecture andAnalysis ofSystems. Amongthem,Iwouldliketomention Re´miSharrock, Jean-Marie
Codol and Aure´lien Gonzalez, for whom Ioffer myfondest regards forall of the timewehave passed
together. Ihopeourfriendship continues on.
I wish to thank my friends outside work and whom I get to call my second family: Alaa Allouch,
HoussamArbess,YahyaSalma,NadimNasreddineandseveralothers. Youweretherenexttomewhen
Iwasinneed. Withoutyourcompany,lifewouldn’thavebeenthesameabroad.
Finally, I would like to dedicate this thesis to my parents, Mustafa and Mervat, who have raised,
taught, supported andguidedmethroughout mylife.
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iv
Abstract
Thelastcoupleofyearshaveseenaprofoundevolutioninembeddedarchitectureswiththeintroduction
ofIntegrated ModularAvionics(IMA).Byofferingtoembeddedapplications astandardized execution
and communication support, these architectures have allowed the consequent reduction of physical
weight and complexity. This low level reduction of complexity is opposed by an increased difficulty
in application conception and integration, as managing resource sharing is through numerous configu-
ration parameters. This thesis is devoted to two resource allocation problems that arise in conception
phasesofIMA-basedarchitectures.
The multiprocessor scheduling problem is first addressed for strictly periodic tasks, or in other
words tasks that execute indefinitely in equidistant time intervals. The objective is supposed to be
the maximization of the minimal idle time between two tasks while avoiding overlap in temporal ex-
ecutions. This allows guaranteeing a minimal evolution margin for the task executions. An integer
linear programming based formulation isfirstproposed for this NP-hard problem, integrating allasso-
ciated temporalandresource constraints. Toextend scalability, aheuristic inspired fromGameTheory
is equally introduced. In this heuristic, each task adopts a scheduling that maximizes its proper util-
ity function (related to the evolution margin of tasks). The convergence of this algorithm towards an
equilibrium point, where no task has an interest in modifying its strategy, is shown in addition to the
presence ofaglobally optimal equilibrium. Theobtained numerical results show thatthis algorithm is
muchfasterthantheexactmethodandgivesagoodapproximation. Tofurtheramelioratethequalityof
obtained solutions, multi-start methods canbeapplied tothisalgorithm tosupplyprobabilistic guaran-
teesontheoptimalityofattainedequilibria.
In the second part of the thesis, the message routing problem between avionic functions in the
AFDXnetworkisconsidered. ThisnetworkallowsthetransmissionofEthernetframesinwhatiscalled
virtuallinks(VL).EachVLcanbeseenasamulticasttreeallowingdatatransmissionfromonepointof
thenetworktoseveralothers. Anexactnode-linklinearformulationisfirstintroduced. Thisisfollowed
bythepropositionofatwo-levelheuristicthatcompromisesbetweenthefairloaddistributionanddelay
minimization inthenetwork. Theobtained results showthatsolutions obtained bytheheuristic canbe
veryclosetothoseoftheexactmethodwhilesignificantly ameliorating communication delays.
v
vi
CONTENTS
Re´sume´ e´tendu 1
Introduction 23
1 Resourceallocation inavionicsystems 27
1.1 Evolution ofavionic systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.2 TheIntegratedModularAvionics architecture . . . . . . . . . . . . . . . . . . . . . . 28
1.2.1 Architecture components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.2.2 Theavionics AFDXnetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.2.3 Partitionsegregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.3 Overviewonthesoftwaredevelopment processdesigninavionics . . . . . . . . . . . 35
1.3.1 Requirements analysis phase . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
1.3.2 Systemdesignphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
1.3.3 Architecture design phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
1.3.4 Detaileddesignphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.5 Codingphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.6 Unittestingphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.7 Integration testingphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.8 Systemtestingphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.9 Acceptance testingphase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.4 TheresearchprojectSATRIMMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
1.5 Objectivesofthestudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
1.5.1 Scheduling objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
1.5.2 VirtualLinkroutingobjectives . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2 Stateoftheart 43
2.1 Introduction toreal-timesystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
vii
viii CONTENTS
2.1.1 Hardreal-timesystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.1.2 Softreal-timesystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2 Generalities onreal-timescheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2.1 Real-timetasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.2.2 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.2.3 Classesofscheduling problems . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.2.4 Non-preemption inscheduling problems . . . . . . . . . . . . . . . . . . . . . 49
2.2.5 Schedulability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.3 Embeddedsystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.3.1 Memorymanagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.3.2 Distributed systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.3.3 Energyconsumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.3.4 Fault-tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.3.5 Otherconsiderations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.4 Complexityofscheduling problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.5 Real-timescheduling algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.5.1 Uniprocessor scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.5.2 Multiprocessor scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.5.3 Non-preemptive andstrictly periodicmultiprocessor scheduling . . . . . . . . 59
2.6 Theoretic conceptsforthethesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.6.1 Particularities ofthestudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.6.2 Someknownsolution strategies . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.6.3 Gametheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3 MILPformulation oftheschedulingproblem 67
3.1 Problemdefinitions andmodeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.1.1 Modulemodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1.2 Partitionmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.1.3 Communication model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.2.1 Temporalscheduling constraints . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.2.2 Resourceconstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.2.3 Communication delayorlatencyconstraints . . . . . . . . . . . . . . . . . . . 75
3.2.4 Formulationasamixedintegerlinearprogram . . . . . . . . . . . . . . . . . 77
3.3 Pre-treatment usinggraphtheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4 Abest-responseschedulingalgorithm 85
4.1 Uniprocessor orsinglemodulescheduling . . . . . . . . . . . . . . . . . . . . . . . . 86
4.1.1 Uniprocessor best-response . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.1.2 Properties ofthebest-response algorithm . . . . . . . . . . . . . . . . . . . . 88
4.1.3 Computingthebest-response . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
CONTENTS ix
4.1.4 Computingtheintersection points . . . . . . . . . . . . . . . . . . . . . . . . 95
4.2 Multiprocessor Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.2.1 Initialallocation inthemultiprocessor setting . . . . . . . . . . . . . . . . . . 101
4.3 Multi-start withbayesian stopping rules . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.3.1 Stoppingrules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.4.1 Largescaleandindustrial applications . . . . . . . . . . . . . . . . . . . . . . 107
4.4.2 Multi-start results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.4.3 Processor minimizationcontext . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5 VirtualLinkrouting 115
5.1 VirtualLinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5.2 Problemdescription andrelatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3 Formaldefinition oftheproblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.4 Anexactnode-link formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.5 Two-levelVLroutingalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.5.1 Steinertreeproblem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.5.2 IterativeLoadingofSteinerTrees(ILST) . . . . . . . . . . . . . . . . . . . . 122
5.5.3 VirtualLinkRoutingOptimization (VLRO) . . . . . . . . . . . . . . . . . . . 124
5.6 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Conclusion 129
A Maximalindependentsetandmaximumcliqueproblems 133
B Listofpublications 135
Bibliography 137