{"id":1084,"date":"2018-09-28T14:10:00","date_gmt":"2018-09-28T12:10:00","guid":{"rendered":"https:\/\/samovar2022.int-evry.fr\/index.php\/2018\/09\/28\/optimisation-des-ressources-autonomiques-pour-la-gestion-des-processus-metier-a-base-de-services-dans-le-cloud\/"},"modified":"2020-09-04T18:45:45","modified_gmt":"2020-09-04T16:45:45","slug":"optimisation-des-ressources-autonomiques-pour-la-gestion-des-processus-metier-a-base-de-services-dans-le-cloud","status":"publish","type":"post","link":"https:\/\/samovar.telecom-sudparis.eu\/index.php\/2018\/09\/28\/optimisation-des-ressources-autonomiques-pour-la-gestion-des-processus-metier-a-base-de-services-dans-le-cloud\/","title":{"rendered":"\u00ab Optimisation des ressources autonomiques pour la gestion des processus m\u00e9tier \u00e0 base de services dans le Cloud \u00bb"},"content":{"rendered":"

AVIS DE SOUTENANCE de Madame Leila HADDED<\/strong><\/p>\n

Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Universit\u00e9 Paris-Saclay, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : Informatique
\n\u00ab Optimisation des ressources autonomiques pour la gestion des processus m\u00e9tier \u00e0 base de services dans le Cloud \u00bb<\/p>\n

le SAMEDI 6 OCTOBRE 2018 \u00e0 9h00
\n\u00e0 Salle de conf\u00e9rences
\nFST Campus Universitaire El Manar – 2092 Tunis – TUNISIE<\/strong><\/p>\n

Membres du jury :<\/strong><\/p>\n\n\n\n\n\n\n\n\n
M. Xavier BLANC<\/td>\n Professeur, Universit\u00e9 de Bordeaux, FRANCE – Rapporteur<\/td>\n<\/tr>\n
Mme Leila JEMNI BEN AYED<\/td>\n Professeure, Ecole Nationale des Sciences de l’Informatique, TUNISIE – Rapporteur<\/td>\n<\/tr>\n
M. Samir TATA<\/td>\n Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de th\u00e8se<\/td>\n<\/tr>\n
M. Faouzi BEN CHARRADA<\/td>\n Professeur, Facult\u00e9 des Sciences de Tunis, TUNISIE – Codirecteur de th\u00e8se<\/td>\n<\/tr>\n
Mme HANNA KLAUDEL<\/td>\n Professeure, Universit\u00e9 d’Evry Val d’Essonne, FRANCE – Examinatrice<\/td>\n<\/tr>\n
M. Habib OUNELLI<\/td>\n Professeur, Facult\u00e9 des Sciences de Tunis, TUNISIE – Examinateur<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

R\u00e9sum\u00e9 :<\/strong><\/p>\n

Le Cloud Computing est un nouveau paradigme qui fournit des ressources informatiques sous forme de services \u00e0 la demande via internet fond\u00e9 sur le mod\u00e8le de facturation pay-per-use. Il est de plus en plus utilis\u00e9 pour le d\u00e9ploiement et l\u2019ex\u00e9cution des processus m\u00e9tier en g\u00e9n\u00e9ral et des processus m\u00e9tier \u00e0 base de services (SBPs) en particulier. Les environnements cloud sont g\u00e9n\u00e9ralement tr\u00e8s dynamiques. \u00c0 cet effet, il devient indispensable de s\u2019appuyer sur des agents intelligents appel\u00e9s gestionnaires autonomiques (AMs), qui permettent de rendre les SBPs capables de se g\u00e9rer de fa\u00e7on autonome afin de faire face aux changements dynamiques induits par le cloud. Cependant, les solutions existantes sont limit\u00e9es \u00e0 l\u2019utilisation soit d\u2019un AM centralis\u00e9, soit d\u2019un AM par service pour g\u00e9rer un SBP. Il est \u00e9vident que la deuxi\u00e8me solution repr\u00e9sente un gaspillage d\u2019AMs et peut conduire \u00e0 la prise de d\u00e9cisions de gestion contradictoires, tandis que la premi\u00e8re solution peut conduire \u00e0 des goulots d\u2019\u00e9tranglement au niveau de la gestion du SBP. Par cons\u00e9quent, il est essentiel de trouver le nombre optimal d\u2019AMs qui seront utilis\u00e9s pour g\u00e9rer un SBP afin de minimiser leur nombre tout en \u00e9vitant les goulots d\u2019\u00e9tranglement. De plus, en raison de l\u2019h\u00e9t\u00e9rog\u00e9n\u00e9it\u00e9 des ressources cloud et de la diversit\u00e9 de la qualit\u00e9 de service (QoS) requise par les SBPs, l\u2019allocation des ressources cloud pour ces AMs peut entra\u00eener des co\u00fbts de calcul et de communication \u00e9lev\u00e9s et\/ou une QoS inf\u00e9rieure \u00e0 celle exig\u00e9e. Pour cela, il est \u00e9galement essentiel de trouver l\u2019allocation optimale des ressources cloud pour les AMs qui seront utilis\u00e9s pour g\u00e9rer un SBP afin de minimiser les co\u00fbts tout en maintenant les exigences de QoS. Dans ce travail, nous proposons un mod\u00e8le d\u2019optimisation d\u00e9terministe pour chacun de ces deux probl\u00e8mes. En outre, en raison du temps n\u00e9cessaire pour r\u00e9soudre ces probl\u00e8mes qui cro\u00eet de mani\u00e8re exponentielle avec la taille du probl\u00e8me, nous proposons des algorithmes quasi-optimaux qui permettent d\u2019obtenir de bonnes solutions dans un temps raisonnable.<\/p>\n

Abstract :<\/strong><\/p>\n

Cloud Computing is a new paradigm that provides computing resources as a service over the internet in a pay-per-use model. It is increasingly used for hosting and executing business processes in general and service-based business processes (SBPs) in particular. Cloud environments are usually highly dynamic. Hence, executing these SBPs requires autonomic management to cope with the changes of cloud environments implies the usage of a number of controlling devices, referred to as Autonomic Managers (AMs). However, existing solutions are limited to use either a centralized AM or an AM per service for managing a whole SBP. It is obvious that the latter solution is resource consuming and may lead to conflicting management decisions, while the former one may lead to management bottlenecks. An important problem in this context, deals with finding the optimal number of AMs for the management of an SBP, minimizing costs in terms of number of AMs while at the same time avoiding management bottlenecks and ensuring good management performance. Moreover, due to the heterogeneity of cloud resources and the diversity of the required quality of service (QoS) of SBPs, the allocation of cloud resources to these AMs may result in high computing costs and an increase in the communication overheads and\/or lower QoS. It is also crucial to find an optimal allocation of cloud resources to the AMs, minimizing costs while at the same time maintaining the QoS requirements. To address these challenges, in this work, we propose a deterministic optimization model for each problem. Furthermore, due to the amount of time needed to solve these problems that grows exponentially with the size of the problem, we propose near-optimal algorithms that provide good solutions in reasonable time.<\/p>\n","protected":false},"excerpt":{"rendered":"

AVIS DE SOUTENANCE de Madame Leila HADDED Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Universit\u00e9 Paris-Saclay, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : Informatique \u00ab Optimisation des ressources autonomiques pour la gestion des processus m\u00e9tier \u00e0 base de services dans le Cloud \u00bb le SAMEDI 6 OCTOBRE 2018 \u00e0 9h00 \u00e0 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