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Enregistrement W7028731145

Global-driven service composition in mobile and pervasive computing

2016· dissertation· en· W7028731145 sur OpenAlexaff

Notice bibliographique

RevueTrinity's Access to Research Output (TARA) (Trinity College Dublin) · 2016
Typedissertation
Langueen
DomaineArts and Humanities
ThématiqueLinguistics and language evolution
Établissements canadiensTrinity College
Organismes subventionnairesnon disponible
Mots-clésUbiquitous computingProvisioningContext-aware pervasive systemsServices computingService (business)Service providerMobile computingMobile QoSService discoveryService delivery framework
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Pervasive computing environments enable access to diverse resources and services over networked computing systems. Mobile systems have the potential to be very active participants in such environments as resource providers, since they are in wide-spread use, and can sense and exchange their operating environments' context data. Service-oriented computing has emerged as an important paradigm in pervasive computing, because it packages heterogeneous resources as services that are discoverable, accessible, and reusable. Services offered by potentially multiple devices can be composed to create a new value-added service. Service provision through service composites is explored in this thesis, particularly in pervasive environments where service providers are mobile and communicate with each other in an ad hoc manner. Mobile service providers are free to join and leave a system, making the availability of the services they provide unpredictable. Service execution may fail because of a previously available service provider's absence at runtime. There is significant potential for improving overall service quality in real-time services provisioning by re-composing better services from the environment including those that may have appeared even during service execution. Mobility also changes the network topology and the links between services, which can lead to execution path loss, and in turn composition failures at runtime. Thus, service composition requires a comprehensive and dynamic discovery model to reason about an appropriate combination of services that match the given functionality, as well as an efficient mechanism that adapts composite services to dynamic environments. Existing research on service-oriented computing has led to automatic planning, adaptive composition and composition recovery to tackle dynamic environments, but requires global service knowledge or a view of the real-time service links. Given mobile devices' limited communication ranges, the network topology changes quickly when devices are roaming, and keeping such system views up-to-date leads to additional communication, maintenance overhead, and may delay the composition process. This thesis presents a fully decentralized services composition model that supports flexible service discovery and execution in mobile pervasive environments. The model is goal-driven, focusing on time-efficient service provisioning to reduce the interference of topology changes. This goal-driven approach achieves flexible service discovery by dynamically planning a service workflow, which supports not only sequential service composites but also complex composites such as parallel or hybrid service flows. Service links' reliability and quality of service issues are considered when selecting services for invocation, which reduces the possibility of execution failures and the effort required for maintaining backup services for composition recovery. If necessary, failure recovery is attempted by adaptable OR-split transitions in the service workflow. The model has been evaluated using both simulation and a prototype case study. Evaluation metrics include measurements of composition success rates under various mobility models, and the composition model's scalability and performance. Simulation results illustrate both the strengths and the limitations of the proposed mechanism in dynamic pervasive computing environments, under different network density and composite complexity conditions. The prototype case study demonstrates this approach's feasibility on real mobile devices.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,579
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,000
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,092
Tête enseignante GPT0,390
Écart entre enseignants0,298 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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