Resource management is at the heart of many diverse science and engineering research areas. Although the general notion of what constitutes a resource entity seems similar in different research areas, their types, characteristics, and constraints governing their behavior are vastly different depending on the particular domain of research and the nature of the research itself. Often research related to resource modeling and management focus on largely homogeneous resources in a relatively simplified model of the real world. The problem becomes much more challenging to deal with when working with a complex real life domain with many heterogeneous resource types and intricate constraints. In this dissertation, we have looked at the modeling and management of resource instances and tried to develop a better sense of what makes them different from other objects in a system. As part of this work, We formally define the general resource management problem, identify its major sub problem areas and their associated complexities, and look at the problem in the context of a particularly complex and dynamic environment, namely the emergency department (ED) of a hospital. We propose an approach to the problem and some of its complexities by presenting an overall unifying view, as well as tools and methods for dealing with, this pervasive, yet surprisingly under examined, type of entity, i.e. resources. We have discovered that one of the discerning characteristics of resource instances in complex and dynamic environments seem to be their dynamic capability profile that may changes depending on system context. This, in turn, often results in complex substitutability relationship amongst resource instances. We have identified four major sub-problem areas that can provide a holistic view of any resource management service. These separate, yet interconnected, areas of con- cerns include resource modeling, resource request specification, resource constraint management, and resource allocation. Resource modeling involves capturing of re- source characteristics and their potentially dynamic behavior. Request definitions describe how resource users specify requirements for resources in a particular do- main. In most domains, there are constraints that need to be satisfied while serving resources to fulfill specific requests. The fourth area of concerns, the allocation of resources, is a complex component with multiple subcomponents that closely inter- act with each other. In this thesis, we have described an architecture for a exible resource management service based on the above described separation of concerns. We have proposed some simple, yet effective, techniques for modeling resource in- stances, specifying resource requests, specifying and managing resource constraints, and allocating resource instances to meet a resource demand characterized by a con- tinuous stream of requests. Using our proposed design, we have developed ROMEO, a resource management service and customized it to serve a task coordination frame- work based on Litlle-JIL process definition language. Our work then concentrated on evaluating the effectiveness of ROMEO in supporting simulations and executions of complex processes. For this evaluation purpose, we developed a simulation infras- tructure named JSim on top of Juliette, Little-JIL's execution environment. We ran a variety of simulations of patient care processes in EDs using our ROMEO-JSim infrastructure. We also used ROMEO to support the actual execution (rather than just the simulation) of a large mediation process. A central premise, hypothesized and explored in this thesis, is a novel way of thinking about resource instances in dynamic domains, namely defining them with a set of guarded capabilities, some of which may be dependent on the execution state of the system. This has led us to think about how to represent execution states of a running system and what types of system state information might be important for representing the guard functions on the capabilities of a resource instance that define the resource instance's ability to satisfy a request at a given execution state of the system. We have also identified a small set of common types of attributes of resource instances that seem able to support specification of a large variety of resource instances in complex domains. We believe that our research supports our hypothesis that specifying resource instances as having sets of guarded capabilities provides a useful abstraction for modeling many of the complex dynamic behaviors of resource instances in such domains as hospital EDs.

Since the ability of Workforce Management Systems to handle mobility induced challenges of mobile environments like data-communication cut-offs, reduced network bandwidth, and security concerns improved recently, the optimization efforts of mobile enterprises increasingly focus on the organizational setup of their mobile environment. This includes issues like, e.g., the dimension and staffing of regional subdivisions, qualification balance of the workforce, and resource allocation strategies. While this multitude of possible adjustment parameters for optimization prevents from the analytical prediction of organizational change efforts, simulation is a promising approach to analyze mobile environments and their change. In this work we present a formal model representing a generalization of mobile environments. This model can be utilized to examine the cost situation and performance of both real mobile enterprises and projected future development scenarios of such enterprises. The model is developed using colored petri nets (CPN) and the software suite CPN Tools. We show that our model is capable of predicting the outcomes of organizational change projects by the utilization of simulation and present a validation of our model based on real-world data of a German gas and power supply.

Le Cloud Computing ou "informatique en nuage" est un nouveau paradigme émergeant pour l’exploitation des services informatiques distribuées à large échelle s’exécutant à des emplacements géographiques répartis. Ce paradigme est de plus en plus utilisé pour le déploiement et l’exécution des applications en général et des applications à base de services en particulier. Les applications à base de services sont décrites à l’aide du standard Service Component Architecture (SOA) et consistent à inter-lier un ensemble de services élémentaires et hétérogènes en utilisant des spécifications de composition de services appropriées telles que Service Component Architecture (SCA) ou encore Business Process Execution Language (BPEL). Provisionner une application dans le Cloud consiste à : (1) allouer les ressources dont elle a besoin pour s’exécuter, (2) déployer ses sources sur les ressources allouées et (3) démarrer l’application. Cependant, les solutions Cloud existantes sont limitées en termes de plateformes d’exécution. Ils ne peuvent pas toujours satisfaire la forte hétérogénéité des composants des applications à base de services. Pour remédier à ces problèmes, les mécanismes de provisioning des applications dans le Cloud doivent être reconsidérés. Ces mécanismes doivent être assez flexibles pour supporter la forte hétérogénéité des composants sans imposer de modifications et/ou d’adaptations du côté du fournisseur Cloud. Elles doivent également permettre le déploiement automatique des composants dans le Cloud. Si l’application à déployer est mono-composant, le déploiement est fait automatiquement et de la même manière, et ce quelque soit le fournisseur Cloud choisi. Si l’application est à base de services hétérogènes, des fonctionnalités appropriées doivent être mises à la disposition des développeurs pour qu’ils puissent définir et créer les ressources nécessaires aux composants avant de déployer l’application. Dans ce travail, nous proposons une approche appelée SPD permettant le provisioning des applications à base de services dans le Cloud. L’approche SPD est constituée de 3 étapes : (1) découper des applications à base de services en un ensemble de services élémentaires et autonomes, (2) encapsuler les services dans des micro-conteneurs spécifiques et (3) déployer les micro-conteneurs dans le Cloud. Pour le découpage, nous avons élaboré un ensemble d’algorithmes formels assurant la préservation de la sémantique des applications une fois découpées. Pour l’encapsulation, nous avons réalisé des prototypes de conteneurs de services permettant l’hébergement et l’exécution des services avec seulement le minimum des fonctionnalités nécessaires. Pour le déploiement, deux cas sont traités i.e. déploiement sur une infrastructure Cloud (IaaS) et déploiement sur une plateforme Cloud (PaaS). Pour automatiser le processus de déploiement, nous avons défini : (i) un modèle de description des ressources unifié basé sur le standard Open Cloud Computing Interface (OCCI) permettant de décrire l’application et ses ressources d’une manière générique quelque soit la plateforme de déploiement cible et (ii) une API appelée COAPS implémentant ce modèle et permettant de l’approvisionnement et la gestion des applications en utilisant des opérations génériques quelque soit la plateforme cible / Cloud Computing is a new supplement, consumption, and delivery model for IT services based on Internet protocols. It is increasingly used for hosting and executing applications in general and service-based applications in particular. Service-based applications are described according to Service Oriented Architecture (SOA) and consist of assembling a set of elementary and heterogeneous services using appropriate service composition specifications like Service Component Architecture (SCA) or Business Process Execution Language (BPEL). Provision an application in the Cloud consists of allocates its required resources from a Cloud provider, upload source codes over their resources before starting the application. However, existing Cloud solutions are limited to static programming frameworks and runtimes. They cannot always meet with the application requirements especially when their components are heterogeneous as service-based applications. To address these issues, application provisioning mechanisms in the Cloud must be reconsidered. The deployment mechanisms must be flexible enough to support the strong application components heterogeneity and requires no modification and/or adaptation on the Cloud provider side. They also should support automatic provisioning procedures. If the application to deploy is mono-block (e.g. one-tier applications), the provisioning is performed automatically and in a unified way whatever is the target Cloud provider through generic operations. If the application is service-based, appropriate features must be provided to developers in order to create themselves dynamically the required resources before the deployment in the target provider using generic operations. In this work, we propose an approach (called SPD) to provision service-based applications in the Cloud. The SPD approach consists of 3 steps: (1) Slicing the service-based application into a set of elementary and autonomous services, (2) Packaging the services in micro-containers and (3) Deploying the micro-containers in the Cloud. Slicing the applications is carried out by formal algorithms that we have defined. For the slicing, proofs of preservation of application semantics are established. For the packaging, we performed prototype of service containers which provide the minimal functionalities to manage hosted services life cycle. For the deployment, both cases are treated i.e. deployment in Cloud infrastructure (IaaS) and deployment in Cloud platforms (PaaS). To automate the deployment, we defined: (i) a unified description model based on the Open Cloud Computing Interface (OCCI) standard that allows the representation of applications and its required resources independently of the targeted PaaS and (ii) a generic PaaS application provisioning and management API (called COAPS API) that implements this model