Notice bibliographique
Résumé
In this thesis, the problem of decontaminating networks from Black Viruses (BVs) using a team of system mobile agents, i.e., the BVD problem, is investigated.The BV is a dynamic harmful process which, like the extensively studied black hole (BH), destroys any agent arriving at the network site where it resides; when that occurs, unlike a black hole which is static by definition, a BV moves, spreading to all the neighbouring sites, thus increasing its presence in the network.The initial location of BV is unknown a priori.The objective is to permanently remove any presence of the BV from the network with minimum number of site infections (and thus casualties) and prevent any previously decontaminated node from becoming infected again.The BVD problem is first studied in the systems with only one BV.Initial investigations are for some common classes of interconnection networks: (multidimensional) grids, tori, and hypercubes.Optimal solutions are proposed and their complexities are analyzed in terms of node infections, agent team size, and movements.After understanding the basic properties of the decontamination process in these special graphs, the BVD problem is studied in arbitrary networks.Finally research is extended to Multiple BV Decontamination problem (MBVD) both in arbitrary graphs and in special topologies.To help understand the behavior of the protocol developed and support complexity analysis, an experimental study is performed using the simulator for reactive distributed algorithms DisJ.A large number of simulations are carried out on various sizes of graphs with many connectivity densities.The simulation runs show that the propose protocol beats random search; they also disclose many interesting behaviors, and validate the analytical complexity results.The simulation results also provide iii deep understanding on the influence of graph connectivity density and graph size on complexities, i.e., movement, time, and agent size.Finally conclusion remarks are presented and future researches are proposed.ivFirstly, I would like to express my sincere gratitude to my advisors Professors Nicola Santoro and Paola Flocchini for their enthusiastic motivation, judicious and enlightening advices, and patient guidance throughout this research work!Their passion for distributed computing, dedication to knowledge acquiring and science developing, and an unrelenting attitude to detail all inspire me.On the other hand, their modest academic style creates an excellent research environment.They carefully selected my courses and topics, and at the same time, provided me enough flexibility to suit my work and life.Our meetings and discussions always take away their precious lunch-break time... Without their continuous support of my Ph.D. study and related research, it would not be possible for me to complete this work.Last but not the least, I would like to thank my wife, Yan Gong, and my daughter, Jessica Cai.Working full-time and supporting a family, and at the same time, studying and doing research part-time for my Ph.D. degree are definitely challenge to me.Without
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».