1,645 research outputs found

    On distributed virtual network embedding with guarantees

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    To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst-case efficiency of (1-(1/e)) relative to the optimal node embedding, or VNET embedding if virtual links are mapped to exactly one physical link. This bound is optimal, that is, no better polynomial-time approximation algorithm exists, unless P=NP. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared to existing distributed VNET embedding solutions, and we show how by appropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.CNS-0963974 - National Science Foundationhttp://www.cs.bu.edu/fac/matta/Papers/ToN-CAD.pdfAccepted manuscrip

    On distributed virtual network embedding with guarantees

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    To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974

    On distributed virtual network embedding with guarantees

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    To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974

    An Improved Algorithm for Fixed-Hub Single Allocation Problem

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    This paper discusses the fixed-hub single allocation problem (FHSAP). In this problem, a network consists of hub nodes and terminal nodes. Hubs are fixed and fully connected; each terminal node is connected to a single hub which routes all its traffic. The goal is to minimize the cost of routing the traffic in the network. In this paper, we propose a linear programming (LP)-based rounding algorithm. The algorithm is based on two ideas. First, we modify the LP relaxation formulation introduced in Ernst and Krishnamoorthy (1996, 1999) by incorporating a set of validity constraints. Then, after obtaining a fractional solution to the LP relaxation, we make use of a geometric rounding algorithm to obtain an integral solution. We show that by incorporating the validity constraints, the strengthened LP often provides much tighter upper bounds than the previous methods with a little more computational effort, and the solution obtained often has a much smaller gap with the optimal solution. We also formulate a robust version of the FHSAP and show that it can guard against data uncertainty with little cost

    Algoritmos de aproximaĆ§Ć£o para problemas de localizaĆ§Ć£o e alocaĆ§Ć£o de terminais

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    Orientador: Lehilton Lelis Chaves PedrosaDissertaĆ§Ć£o (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaĆ§Ć£oResumo: No Problema de LocalizaĆ§Ć£o e AlocaĆ§Ć£o de Terminais, a entrada Ć© um espaƧo mĆ©trico composto por clientes, localidades e um conjunto de pares de clientes; uma soluĆ§Ć£o Ć© um subconjunto das localidades, onde serĆ£o abertos terminais, e uma atribuiĆ§Ć£o de cada par de clientes a uma rota, que comeƧa no primeiro cliente, passando em um ou dois terminais, e terminando no segundo cliente. O objetivo Ć© encontrar uma soluĆ§Ć£o que minimize o tamanho de todas as rotas somado com o custo de abertura de terminais. Os algoritmos de aproximaĆ§Ć£o da literatura consideram apenas o caso em que o conjunto de terminais abertos Ć© dado como parte da entrada, e o problema se torna atribuir clientes aos terminais; ou entĆ£o quando o espaƧo Ć© definido em classes especiais de grafos. Neste trabalho, apresentamos o primeiro algoritmo de aproximaĆ§Ć£o com fator constante para o problema de, simultaneamente, escolher localidades para abrir terminais e atribuir clientes a estes. A primeira parte desta dissertaĆ§Ć£o cria algoritmos de aproximaĆ§Ć£o para diversas variantes do problema. A estratĆ©gia principal Ć© reduzir os problemas de localizaĆ§Ć£o e alocaĆ§Ć£o de terminais aos problemas clĆ”ssicos de localidades, como o problema de localizaĆ§Ć£o de instalaƧƵes e o problema das k-medianas. A reduĆ§Ć£o transforma uma instĆ¢ncia de localizaĆ§Ć£o e alocaĆ§Ć£o de terminais em uma instĆ¢ncia de um destes problemas, que entĆ£o Ć© resolvida usando algoritmos de aproximaĆ§Ć£o jĆ” existentes na literatura. A saĆ­da do algoritmo induz uma soluĆ§Ć£o para o problema original, com uma perda constante no fator de aproximaĆ§Ć£o. Na segunda parte, o foco Ć© o Problema de LocalizaĆ§Ć£o e AlocaĆ§Ć£o ƚnica de Terminais (SAHLP), que Ć© uma variaĆ§Ć£o em que cada cliente deve estar conectado a apenas um terminal, alĆ©m de nĆ£o haver limite na quantidade de terminais abertos. A principal contribuiĆ§Ć£o Ć© um algoritmo 2.48-aproximado para o SAHLP, baseado em arredondamento de uma nova formulaĆ§Ć£o de programa linear para o problema. O algoritmo Ć© composto por duas fases: na primeira, a soluĆ§Ć£o fracionĆ”ria Ć© escalada e um subconjunto de terminais Ć© aberto, e na segunda, atribuĆ­mos clientes aos terminais abertos. A primeira fase segue o formato padrĆ£o de filtering para problemas de localidades. A segunda, no entanto, exigiu o desenvolvimento de novas ideias e Ć© baseada em mĆŗltiplos critĆ©rios para realizar a atribuiĆ§Ć£o. A principal tĆ©cnica atribui cada cliente ao terminal aberto mais prĆ³ximo, se este estiver em sua vizinhanƧa; caso contrĆ”rio, o cliente se conecta ao terminal que melhor balanceia mĆŗltiplos custos, relacionados Ć  distĆ¢ncia entre elesAbstract: In the Hub Location Problem (HLP), the input is a metric space composed of clients, locations and a set of pairs of clients; a solution is a subset of locations to open hubs and an assignment for each pair of clients to a route starting in the first client, passing through one or two hubs and ending in the second client. The objective is to find a solution that minimizes the length of all routes plus the cost of opening hubs. The currently known approximation algorithms consider only the case in which the set of hubs is given as part of the input and the problem is assigning clients to hubs; or when the space is defined on special classes of graphs. In this work, we present the first constant-factor approximation algorithms for the problem of, simultaneously, selecting hubs and allocating clients. The first part of the thesis derives approximation algorithms for several variants of the problem. The main strategy is to reduce the hub location problems to classical location problems, such as Facility Location and k-Median. The reduction transforms an instance of hub location into an instance of a corresponding location problem, which is then solved by known approximation algorithm. The algorithmĀæs output induces a solution of the original problem within a constant loss in the approximation ratio. In the second part, we focus on the Single Allocation Hub Location Problem (SAHLP), that is the variant in which a client must be connected to only one hub and there is no limit on the number of open hubs. Our main contribution is a 2.48-approximation algorithm for the SAHLP, based on the rounding of a new linear programming formulation. The algorithm is composed of two phases: in the first one, we scale the fractional solution and open a subset of hub locations, and in the second one, we assign clients to open hubs. The first phase follows the standard filtering framework for location problems. The latter, however, demanded the development of new ideas and is based on a multiple criteria assignment. The main technique is assigning a client to a closest open hub only if there are near open hubs, and otherwise selecting the hub which balances multiple costsMestradoCiĆŖncia da ComputaĆ§Ć£oMestre em CiĆŖncia da ComputaĆ§Ć£o2016/12006-1CAPESFAPES

    Location models for airline hubs behaving as M/D/c queues

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    Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value Ī±\alpha. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.Hub location, congestion, tabu-search

    A bi-objective hub-and-spoke approach for reconfiguring Web communities

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    Web communities in general grow naturally, thus creating unbalanced network structures where a few domains centralise most of the linkups. When one of them breaks down, a significant part of the community might be unable to communicate with the remaining domains. Such a situation is highly inconvenient, as in the case of wishing to pursue distribution policies within the community, or for marketing purposes. In order to reduce the damages of such an occurrence, the Web community should be reconfigured, in such a way that a complete sub-network of main domains -the hubs - is identified and that each of the other domains of the community - the spokes - is doubly linked at least with a hub. This problem can be modellised through a bi-objective optimisation problem, the Web Community Reconfiguring Problem, which will be presented in this paper. A bi-objective mixed binary formulation will also be shown, along with a brief description of GRASP, tabu search and hybrid heuristics which were developed to find feasible solutions to the problem, possibly efficient solutions to the bi-objective problem. A computational experiment is reported, involving comparison of these metaheuristics when applied to several Web communities, obtained by crawling the Web and using epistemic boundaries and to other randomly generated ones. The heuristics revealed excellent quality for the small dimension cases whose efficient solutions were roughly all determined. As for the other medium and higher dimension instances, the heuristics were successful in building a wide variety of feasible solutions that are candidate efficient solutions. The best behaviour was attained with the GRASP and the GRASP and tabu hybrid search. Comparison of some metrics before and after reconfiguration confirmed that the final structures are more balanced in terms of degree distribution reinforcing the connecting effect imposed by the reconfiguration process

    Trade-offs between the stepwise cost function and its linear approximation for the modular hub location problem

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    There exist situations where the transportation cost is better estimated as a function of the number of vehicles required for transporting a load, rather than a linear function of the load. This provides a stepwise cost function, which defines the so-called Modular Hub Location Problem (MHLP, or HLP with modular capacities) that has received increasing attention in the last decade. In this paper, we consider formulations to be solved by exact methods. We show that by choosing a specific generalized linear cost function with slope and intercept depending on problem data, one minimizes the measurement deviation between the two cost functions and obtains solutions close to those found with the stepwise cost function, while avoiding the higher computational complexity of the latter. As a side contribution, we look at the savings induced by using direct shipments in a hub and spoke network, given the better ability of a stepwise cost function to incorporate direct transportation. Numerical experiments are conducted over benchmark HLP instances of the OR-library
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