Supporting Cloud Resource Allocation in Configurable Business Process Models

151 Pages·2017·4.02 MB·English

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Thèse de doctorat de l'Université Paris-Saclay
préparée à TELECOM SudParis

ÉCOLE DOCTORALE N°580
Sciences et technologies de l'information et de la communication

Spécialité de doctorat : Informatique

Thèse présentée et soutenue à Evry, le 22/09/2017, par

Emna Hachicha Belghith

Composition du Jury :

Mme Salima Benbernou
Professeur, Université Paris Descartes Rapporteur
M. Claude Godart
Professeur, Université de Lorraine Rapporteur
M. Nejib Ben Hadj-Alouane
Professeur, Université Tunis El Manar Examinateur
Mme Daniela Grigori
Professeur, Université Paris Dauphine Examinateur
M. Kais Klai
Maître de Conférences HdR, Université Paris 13 Examinateur
Mme Amel Maamar
Maître de Conférences HdR, Télécom SudParis Examinateur
M. Walid Gaaloul
Professeur, Télécom SudParis Directeur de thèse
Titre : Supporter l’allocation de ressources Cloud dans les modèles de processus configurables
Mots clés : Modèles de processus configurables, allocation de ressources configurables, Cloud
Computing, web sémantique, social computing, algorithmes génétiques
Résumé : Motivés par l’adaptation aux Dans cette thèse, nous proposons une approche
exigences commerciales et par la réduction des pour supporter la modélisation et la
coûts de maintenance, les organisations configuration de l’allocation des ressources
externalisent leurs processus dans le Cloud. Cloud dans les modèles de processus
Selon l'Institut NIST, Cloud Computing est un configurables. Nous visons à (i) définir une
modèle qui permet aux fournisseurs de partager description unifiée et formelle pour la
leurs ressources et aux utilisateurs d’y accéder de perspective ressource, (ii) assurer une allocation
manière à la demande. Dans un tel de ressource correcte, sans conflits et optimisée,
environnement, l'utilisation de modèles de (iii) aider les fournisseurs de processus à
processus configurables permet aux fournisseurs modéliser leur allocation de ressources
de processus de fournir un processus configurable de manière fine afin d'éviter des
personnalisable entre par différents tenants en résultats complexes, et (iv) optimiser la sélection
fonction de leurs besoins. des ressources Cloud par rapport aux exigences
La conception et la configuration des ressources liées aux propriétés Cloud et QoS.
dans les processus, sont des tâches fastidieuses. Pour ce faire, nous proposons (i) des définitions
D'une part, la perspective ressource est peu formelles pour la gestion des ressources Cloud à
définie, ce qui empêche une interopérabilité l'aide de la sémantique, et (ii) d'étendre les
efficace. D'autre part, la façon dont les ressources processus configurables afin de permettre aux
Cloud peuvent être configurées et intégrées n’a fournisseurs de processus de personnaliser
pas été traitée. l'allocation des ressources selon leurs besoins.

Title : Supporting Cloud resource Allocation in configurable process models
Keywords : Configurable process models, configurable resource allocation, Cloud Computing,
Semantic Web, Social Computing, Genetic Algorithms
Abstract : Motivated by adapting to the rapid In this thesis, we propose an approach for
changing business requirements and reducing supporting the design and configuration of cloud
maintenance costs, organizations are resource allocation in configurable process
outsourcing their processes using Cloud models. We target to (i) define unified formal
resources. Cloud Computing enables users descriptions for the resource perspective, (ii)
sharing and accessing computing resources in an ensure a correct, free-of-conflict and optimized
on-demand way. In such environment, using use of cloud resource consumption, (iii) assist
configurable process models enables Cloud process providers to design their configurable
providers to deliver a customizable process resource allocation in a fine-grained way to
according to tenants needs. avoid complex and large results, and (iv)
The design and configuration of resources in optimize the selection of Cloud resources
process models are labor-intensive task. On the against the requirements of the Cloud and QoS
one hand, the resource perspective is poorly properties. To do so, we propose (i) formal
operated which prevent an efficient definitions for cloud resource management
interoperability. On the other hand, the way in using semantics and social techniques, and (ii)
how Cloud resources can be configured and to extend configurable process models to allow
integrated are hardly handled. cloud process providers to customize resource
allocation according to their requirements.

Université Paris-Saclay
Espace Technologique / Immeuble Discovery
Route de l’Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France

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to my family

Acknowledgment
(cid:71)
First of all I thank God for making all things possible for me and giving me
strengthen to go. A thesis journey is not always easy, but with the supervision,
support and encouragement of many people, I have never regretted that started it.
I would like to thank all members of the jury. I thank Professor Claude Godart
and Salima Benbernou for accepting being my thesis reviewers and for their attention
and thoughtful comments. I also thank Professor Daniela Grigori, Professor Amel
Maamar, Professor Kais Klai and Professor Nejib Ben Hadj-Alouane a for accepting
being my thesis examiners.
I would like to express my appreciation and gratitude to my supervisor Walid
Gaaloul. His valuable advice, enthusiasm and constant support during this thesis
allowed me to acquire new understandings and extend my experiences. He was not
only an advisor but also a good friend. Thank you for your guidance, it has been a
true pleasure and I hope that we can continue our collaboration.
Iowemydeepestgratitudeandwarmestaffectiontothemembersofthecomputer
science department of Telecom SudParis. I would like to thank Brigitte Houassine for
her kind help and assistance. A special thank you to my office mates Rami, Nour,
Souha, Nabila, Slim, Abderrahim, Rania and Hayet.
Thanks to my friends especially Wadii, Sirine, her lovely son Aziz, and Manel,
Emna, Taha, Hayet, Soumaya for their emotional support and for all the beautiful
moments we shared in France and Tunisia.
I am forever thankful to my parents especially my dear mother Ahlem, my grand-
mother Mamie Biha, my father Mohamed, my father in law Habib, my loving brother
Amine,myparentsinlaw: BouthainaandGhazi,mybrotherinlawAhmed,mysister
in law Hanen, and their cute children Aziz and Malek, my brother in law Yassine, my
sister in law Doniez. Your encouragement made me go forward and made me want
to succeed. Thank you so much for having faith in me! You supported me without
even you know it. I love you so much.
Iexpressmydeepestgratitudetomylovinghusband,Amine. Hisloveandencour-
agement fostered me to concentrate at work. His understanding and support helped
me to get through many difficult times. To my little angel, my beautiful daughter
Dina whose smile and joy of life were the best encouragement for me. I hope that my
thesis will be a source of pride for you.
I dedicate this thesis to all of you, my wonderful family.
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Abstract
Organizations are recently more and more adopting Process-Aware Information Sys-
tems for managing and executing their service-based processes using process models
referred to as business process models. Motivated by adapting to the rapid changing
business requirements and reducing maintenance costs, organizations are outsourcing
their processes using Cloud Computing resources. Cloud Computing enables users
sharing and accessing computing resources in an ubiquitous, and on-demand way. In
such multi-tenant environment, the resource perspective need to be explicitly defined
in process models.
The integration of the resource perspective especially the cloud resource alloca-
tion is a current interesting topic that increasingly involves many researches in both
academics and industry. The design and configuration of resources whether in simple
or configurable process models are undoubtedly sensitive and labor-intensive task,
in such environment. On the one hand, the resource perspective in Business process
modelsispoorlyoperatedincomparisontootherperspectivessuchasthecontrol-flow,
or the organizational perspective. Although several approaches have been proposed
in the literature, they all targeted human resources rather than cloud resources. This
is due to the lack of formal semantic definitions, which prevent an efficient interoper-
ability. Ontheotherhand, despiteofthefactthattheconceptofconfigurableprocess
models is highly complementary to Cloud Computing, the way in how resources can
be configured and integrated are hardly handled. Few proposals have been suggested
in the literature to support resource variability, and cover required Cloud properties
such as elasticity or multi-tenancy.
In this thesis, we address the above shortcomings by proposing an approach for
supporting the design and configuration of cloud resource allocation in configurable
process models. We target to (i) define unified formal descriptions for the resource
perspective, (ii) ensure a correct, free-of-conflict and optimized use of cloud resource
consumption, and (iii) assist process providers to design their configurable resource
allocation in a fine-grained way to avoid complex and large results. To do so, we
propose (i) formal definitions for cloud resource management using semantics and
social techniques, and (ii) to extend configurable process models in order to allow
cloud process providers to customize resource allocation according to their require-
ments. To validate our approach, we (i) develop three proof of concepts as extensions
of existing business process modeling tools, (ii) perform experiments on real process
models from large datasets. Experimental results show that our approach is feasible,
accurate and has good performance in real use-cases.
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Table of contents
1 Introduction 17
1.1 Research context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 Researchproblem: How to integrate Cloud Resource Allocation in busi-
ness process models? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.2.1 On Formalizing the resource perspective definition . . . . . . . 22
1.2.2 On Supporting and Optimizing the resource variability in con-
figurable process models . . . . . . . . . . . . . . . . . . . . . . 23
1.3 Motivating example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.4 Thesis principles, objectives and contributions . . . . . . . . . . . . . . 27
1.4.1 Thesis principles . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.4.2 Thesis objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.4.3 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . . 28
1.5 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 Preliminaries and Basic Concepts 31
2.1 Process Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1.1 Business Process Model and Notation (BPMN) . . . . . . . . . 32
2.1.2 Configurable BPMN (C-BPMN) . . . . . . . . . . . . . . . . . 33
2.1.3 Process Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.2 Semantic Business Process Modeling . . . . . . . . . . . . . . . . . . . 37
2.2.1 Web Ontology Language for Web Services (OWL-S) . . . . . . 37
2.2.2 General Process Ontology (GPO) . . . . . . . . . . . . . . . . . 38
2.2.3 Business Process Modeling Ontology (BPMO) . . . . . . . . . 39
2.3 Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3.1 Generalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3.2 Open Cloud Computing Interface (OCCI) . . . . . . . . . . . . 42
3 State of the Art 47
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 On Formalizing the resource perspective in Business processes . . . . . 48
3.2.1 Resource perspective in business processes . . . . . . . . . . . . 48
3.2.2 Resource orchestration in Cloud-based business processes . . . 49
3.2.3 Semantics in business process models . . . . . . . . . . . . . . . 50
3.2.4 Social business processes . . . . . . . . . . . . . . . . . . . . . . 51
3.2.5 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3 On supporting and Optimizing the resource variability in configurable
process models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1 Configurable process modeling . . . . . . . . . . . . . . . . . . 54
3.3.2 Resource Variability . . . . . . . . . . . . . . . . . . . . . . . . 55
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