Disrupting terrorist and other covert networks requires identifying and capturing key leaders. Previous research by Martonosi et al. (2009) defines a load metric on vertices of a covert network representing the amount of communication in which a vertex is expected to participate. They suggest that the visibility of a target vertex can be increased by removing other, more accessible members of the network. This report evaluates the feasibility of efficiently calculating the optimal subset of vertices to remove. We begin by proving that the general problem of identifying the optimally load maximizing vertex set removal is NP-complete. We then consider the feasibility of more quickly computing the load maximizing single vertex removal by designing an efficient algorithm for recomputing Gomory- Hu trees. This leads to a result regarding the uniqueness of Gomory- Hu trees with implications towards the feasibility of one approach for Gomory- Hu tree reconstruction. Finally, we propose a warm start algorithm which performs this reconstruction, and analyze its runtime experimentally.

Orientador: Vinicius Amaral Armentano / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-10T01:09:54Z (GMT). No. of bitstreams: 1 Fonseca_SaraLuisadeAndrade_M.pdf: 1363535 bytes, checksum: aa7c01c779a21ea25aa3b603425c92fe (MD5) Previous issue date: 2007 / Resumo: Em 1958, Gomory propôs uma desigualdade válida ou corte a partir do tableau do método simplex para programação linear, que foi utilizado no primeiro método genérico para resolução de problemas de programação inteira. Em 1960, o corte foi estendido para problemas de programação inteira mista. Em 1973, Chvátal sugeriu um corte derivado da formulação original do problema de programação inteira, e devido à equivalência com o corte de Gomory, este passou a ser chamado de corte de Chvátal-Gomory. A importância do corte de Gomory só foi reconhecida em 1996 dentro do contexto do método branch-and-cut para resolução de problemas de programação inteira e programação inteira mista. Desde então, este corte é utilizado em resolvedores comerciais de otimização. Recentemente, diversos cortes novos derivados do corte de Chvátal-Gomory foram propostos na literatura para programação inteira. Este trabalho trata do desenvolvimento de algoritmos para alguns destes cortes, e implementação computacional em um contexto de branch-and-cut, no ambiente do resolvedor CPLEX. A eficácia dos cortes é testada em instâncias dos problemas da mochila multidimensional, designação generalizada e da biblioteca MIPLIB. / Abstract: In 1958, Gomory proposed a valid inequality or cut from the tableau of the simplex method for linear programming, which was used in the first generic method for solving integer programming problems. In 1960, the cut was extended to handle mixed integer programming problems. In 1973, Chvátal suggested a cut that is generated from the original formulation of an integer programming problem, and due to the equivalence with the Gomory cut, it was named Chvátal-Gomory cut. The importance of the Gomory cut was recognized only in 1996 in the context of the branch-and-cut method for solving (mixed) integer programming problems. Today, such a cut is utilized in optimization commercial solvers. Recently, several new cuts derived from the Chvátal-Gomory cut have been proposed in the literature for integer programming. This work deals with the development of algorithms and computational implementations for some of the new proposed cuts, in a context of the branch-and-cut method, by using the CPLEX solver. The efficiency of the cuts is tested on instances of the multi-dimensional knapsack, generalized assignment problems, and instances from the MIPLIB library. / Mestrado / Automação / Mestre em Engenharia Elétrica

<p> A solution to an industrial problem of designing a hydrostatic drive system for logging vehicles is presented. A computer program using Land and Doig's method of Branch and Bound Mixed Integer Programming is used to obtain an optimal solution to the problem. A comprehensive users guide to allow the use of the program by unsophisticated users is provided. </p> <p> An attempted alternate method of solution using the Gomory method of Integer Programming is presented and reasons for its failure discussed. </p> / Thesis / Master of Engineering (MEngr)

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L'informatique en nuage (Cloud Computing) est un modèle à grande échelle et en évolution continue, permettant le provisionnement et l'utilisation des ressources informatiques à la demande, selon un modèle rentable de facturation à l'usage "pay-as-you-go". Ce nouveau paradigme a rapidement révolutionné l'industrie IT et a permis de nouvelles tendances en matière de prestation de services informatiques, y compris l'externalisation des infrastructures IT vers des prestataires tiers spécialisés. Cependant, la nature multi-utilisateur des plateformes d'hébergement, ainsi que la complexité des demandes, soulèvent plusieurs défis liés à la gestion des ressources Cloud. Malgré l'attention croissante portée à ce sujet, la plupart des efforts ont été axés sur des solutions centrées utilisateur, et malheureusement beaucoup moins sur les difficultés rencontrées par les fournisseurs pour maximiser leurs bénéfices. Dans ce contexte, la fédération de Cloud a été récemment proposée comme une solution clé pour répondre à l'augmentation et la fluctuation des charges de travail. Les fournisseurs ayant des besoins complémentaires en ressources au fil du temps, peuvent collaborer et partager leurs infrastructures respectives via l'externalisation ("Outsourcing") pour mieux satisfaire les demandes et exigences des utilisateurs. Cette thèse aborde le problème d'optimisation du profit via la fédération et l'allocation optimale des ressources parmi plusieurs fournisseurs d'infrastructures Cloud. L'étude examine les principaux défis et opportunités liés à la maximisation des revenus dans une fédération de Clouds, et définit des stratégies efficaces pour diriger les fournisseurs dans leurs décisions de coopération. Le but est de fournir des algorithmes qui automatisent la sélection du plan d'allocation le plus rentable, qui satisfait à la fois la demande des utilisateurs et les exigences de mise en réseau. Nous visons des modèles d'allocation génériques et robustes qui répondent aux nouvelles tendances Cloud, et de traiter les requêtes simples ainsi que complexes nécessitant le provisionnement de services composites avec différentes ressources distribuées et connectées. Conformément aux objectifs de la thèse, nous avons mené une étude approfondie des travaux antérieurs traitant la problématique de provisionnement des ressources d'infrastructure dans les environnements Cloud. L'analyse a porté notamment sur les modèles d'allocation ayant pour objectif la maximisation de profit et les lacunes et défis associés dans les fédérations de Clouds. Dans un deuxième temps, nous avons proposé un programme linéaire en nombre entiers (ILP), pour aider les fournisseurs de services dans leurs décisions de coopération via des actions optimales d'externalisation (outsourcing), d'internalisation (insourcing) et d'allocation en local. Ces différentes décisions d'allocation sont traitées conjointement dans une formule d'optimisation globale qui partitionne les graphes de requêtes entre les membres de la fédération, tout en satisfaisant les exigences de communication entre les services élémentaires. En plus de la topologie des graphes de ressources, ce partitionnement prend en compte les prix dynamiques et les quotas proposés par les membres de la fédération ainsi que les coûts d'hébergement des ressources et de leur mise en réseau. Enfin, nous avons proposé un algorithme heuristique pour améliorer les temps de convergence avec les instances de problèmes à grande échelle. L'approche proposée utilise un algorithme de "clustering" basé sur les arbres de Gomory-Hu pour le partitionnement des graphes et une stratégie de meilleur ajustement (Best-Fit matching) pour l'allocation et le placement des sous-graphes résultants. L'utilisation conjointe de ces deux techniques permet de capturer l'essence du problème d'optimisation et de respecter les différents objectifs fixés, tout en améliorant le temps de convergence vers les solutions quasi-optimales de plusieurs ordres de grandeur / Cloud computing is a steadily maturing large-scale model for providing on-demand IT resources on a pay-as-you-go basis. This emerging paradigm has rapidly revolutionized the IT industry and enabled new service delivery trends, including infrastructure externalization to large third-party providers. The Cloud multi-tenancy architecture raises several management challenges for all stakeholders. Despite the increasing attention on this topic, most efforts have been focused on user-centric solutions, and unfortunately much less on the difficulties encountered by Cloud providers in improving their business. In this context, Cloud Federation has been recently suggested as a key solution to the increasing and variable workloads. Providers having complementary resource requirements over time can collaborate and share their respective infrastructures, to dynamically adjust their hosting capacities in response to users' demands. However, joining a federation makes the resource allocation more complex, since providers have to also deal with cooperation decisions and workload distribution within the federation. This is of crucial importance for cloud providers from a profit standpoint and especially challenging in a federation involving multiple providers and distributed resources and applications. This thesis addresses profit optimization through federating and allocating resources amongst multiple infrastructure providers. The work investigates the key challenges and opportunities related to revenue maximization in Cloud federation, and defines efficient strategies to govern providers' cooperation decisions. The goal is to provide algorithms to automate the selection of cost-effective distributed allocation plans that simultaneously satisfy user demand and networking requirements. We seek generic and robust models able to meet the new trends in Cloud services and handle both simple and complex requests, ranging from standalone VMs to composite services requiring the provisioning of distributed and connected resources. In line with the thesis objectives, we first provide a survey of prior work on infrastructure resource provisioning in Cloud environments. The analysis mainly focuses on profit-driven allocation models in Cloud federations and the associated gaps and challenges with emphasis on pricing and networking issues. Then, we present a novel exact integer linear program (ILP), to assist IaaS providers in their cooperation decisions, through optimal "insourcing", "outsourcing" and local allocation operations. The different allocation decisions are treated jointly in a global optimization formulation that splits resource request graphs across federation members while satisfying communication requirements between request subsets. In addition to the request topology, this partitioning takes into account the dynamic prices and quotas proposed by federation members as well as the costs of resources and their networking. The algorithm performance evaluation and the identified benefits confirm the relevance of resource federation in improving providers' profits and shed light into the most favorable conditions to join or build a federation. Finally, a new topology-aware allocation heuristic is proposed to improve convergence times with large-scale problem instances. The proposed approach uses a Gomory-Hu tree based clustering algorithm for request graphs partitioning, and a Best-Fit matching strategy for subgraphs placement and allocation. Combining both techniques captures the essence of the optimization problem and meets the objectives, while speeding up convergence to near-optimal solutions by several orders of magnitude