On Exploiting Flow Allocation with Rate Adaptation for Green Networking

Network power consumption can be reduced considerably by adapting link data rates to their offered traffic loads. In this paper, we exploit how to leverage rate adaptation for green networking by studying the following flow allocation problem in wired networks: Given a set of candidate paths for each end-to-end communication session, determine how to allocate flow (data traffic) along these paths such that power consumption is minimized, subject to the constraint that the traffic demand of each session is satisfied. According to recent measurement studies, we consider a discrete step increasing function for link power consumption. We address both the single and multiple communication session cases and formulate them as two optimization problems, namely, the Single-session Flow allocation with Rate Adaptation Problem (SF-RAP), and the Multisession Flow Allocation with Rate Adaptation Problem (MFRAP). We first show that both problems are NP-hard and present a Mixed Integer Linear Programming (MILP) formulation for the MF-RAP to provide optimal solutions. Then we present a 2-approximation algorithm for the SF-RAP, and a general flow allocation framework as well as an LP-based heuristic algorithm for the MF-RAP. Simulation results show that the algorithm proposed for the SF-RAP consistently outperforms a shortest path based baseline solution and the algorithms proposed for the MF-RAP provide close-to-optimal solutions

Hierarchical Network Design Using Simulated Annealing

The hierarchical network problem is the problem of nding the least cost net-work, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical net-works are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the net-work. The problem is solved heuristically using simulated annealing which as a sub-algorithm uses a construction algorithm to determine edges and route the demand. Performance for dierent versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to nd solutions of reasonable quality in approximately 1 hour for networks with 100 nodes

An analysis of Regenerator Placement strategies for a Translucent OBS network architecture

Most research works in optical burst switching (OBS) networks do not take into account the impact of physical layer impairments (PLIs) either by considering fully transparent (i.e., using optical 3R regeneration) or opaque (i.e., electrical 3R regeneration) networks. However, both solutions are not feasible for different reasons. In this paper, we propose a novel translucent OBS (T-OBS) network architecture which aims at bridging the gap between the transparent and opaque solutions. In order to evaluate its performance, a formulation of the routing and regenerator placement and dimensioning problem (RRPD) is presented. Since such formulation results in a complex problem, we also propose several alternative heuristic strategies. In particular, we evaluate the trade-off between optimality and execution times provided by these methods. Finally, we conduct a series of simulation experiments that prove that the T-OBS network model proposed effectively deals with burst losses caused by the impact of PLIs and ensures that the overall network performance remains unaffected.Preprin

On Ranking the Relative Importance of Nodes in Physical Distribution Networks

Physical distribution networks are integral parts of modern supply chains. When faced with a question of which node in a network is more important, cost immediately jumps to mind. However, in a world of uncertainty, there are other significant factors which should be considered when trying to answer such a question. The integrity of a network, as well as its robustness are factors that we consider, in making a judgement of importance. We develop algorithms to measure several properties of a class of networks. To accelerate the optimization of multiple related linear programs, we develop a modification of the revised simplex method, which exploits several key aspects to gain efficiency. We combine these algorithms and methods, to give rankings of the relative importance of nodes in networks. In order to better understand the usefulness of our method, we analyse the effect parameter changes have on the relative importance of nodes. We present a large, realistic network, whose nodes we rank in importance. We then vary the network's parameters and observe the impact of each change

Discrete Cost Multicommodity Network Optimization Problems and Exact Solution Methods

International audienceWe first introduce a generic model for discrete cost multicommodity network optimization, together with several variants relevant to telecommunication networks such as: the case where discrete node cost functions (accounting for switching equipment) have to be included in the objective; the case where survivability constraints with respect to single-link and/or single-node failure have to be taken into account. An overview of existing exact solution methods is presented, both for special cases (such as the so-called single-facility and two-facility network loading problems) and for the general case where arbitrary step-increasing link cost-functions are considered. The basic discrete cost multicommodity flow problem (DCMCF) as well as its variant with survivability constraints (DCSMCF) are addressed. Several possible directions for improvement or future investigations are mentioned in the concluding section

Characterizing capital and operational tradeoffs resulting from fiber-to-the-home optical network architecture choice

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-128).This thesis explores the impact of relative lifecycle cost tradeoffs on technology strategy, and characterizes two factors driving these costs: population demographics, and uncertainty in component costs. The methodology developed consists of three novel components which address gaps in the current literature in the areas of large-scale network design, multi-attribute population characterization, and cost modeling. Three technologies representing near, mid, and long-term fiber-to-the-home gigabit passive optical network solutions, and seven implementation strategies are dimensioned for two significantly different population demographics, each representing large coverage regions containing millions of subscribers. The methodology is able to successfully characterize how relative network topologies changed as a function of population attributes, revealing complex cost tradeoffs between technology strategies.by Thomas Rand-Nash.S.M

GMPLS-OBS interoperability and routing acalability in internet

The popularization of Internet has turned the telecom world upside down over the last two decades. Network operators, vendors and service providers are being challenged to adapt themselves to Internet requirements in a way to properly serve the huge number of demanding users (residential and business). The Internet (data-oriented network) is supported by an IP packet-switched architecture on top of a circuit-switched, optical-based architecture (voice-oriented network), which results in a complex and rather costly infrastructure to the transport of IP traffic (the dominant traffic nowadays). In such a way, a simple and IP-adapted network architecture is desired. From the transport network perspective, both Generalized Multi-Protocol Label Switching (GMPLS) and Optical Burst Switching (OBS) technologies are part of the set of solutions to progress towards an IP-over-WDM architecture, providing intelligence in the control and management of resources (i.e. GMPLS) as well as a good network resource access and usage (i.e. OBS). The GMPLS framework is the key enabler to orchestrate a unified optical network control and thus reduce network operational expenses (OPEX), while increasing operator's revenues. Simultaneously, the OBS technology is one of the well positioned switching technologies to realize the envisioned IP-over-WDM network architecture, leveraging on the statistical multiplexing of data plane resources to enable sub-wavelength in optical networks. Despite of the GMPLS principle of unified control, little effort has been put on extending it to incorporate the OBS technology and many open questions still remain. From the IP network perspective, the Internet is facing scalability issues as enormous quantities of service instances and devices must be managed. Nowadays, it is believed that the current Internet features and mechanisms cannot cope with the size and dynamics of the Future Internet. Compact Routing is one of the main breakthrough paradigms on the design of a routing system scalable with the Future Internet requirements. It intends to address the fundamental limits of current stretch-1 shortest-path routing in terms of RT scalability (aiming at sub-linear growth). Although "static" compact routing works fine, scaling logarithmically on the number of nodes even in scale-free graphs such as Internet, it does not handle dynamic graphs. Moreover, as multimedia content/services proliferate, the multicast is again under the spotlight as bandwidth efficiency and low RT sizes are desired. However, it makes the problem even worse as more routing entries should be maintained. In a nutshell, the main objective of this thesis in to contribute with fully detailed solutions dealing both with i) GMPLS-OBS control interoperability (Part I), fostering unified control over multiple switching domains and reduce redundancy in IP transport. The proposed solution overcomes every interoperability technology-specific issue as well as it offers (absolute) QoS guarantees overcoming OBS performance issues by making use of the GMPLS traffic-engineering (TE) features. Keys extensions to the GMPLS protocol standards are equally approached; and ii) new compact routing scheme for multicast scenarios, in order to overcome the Future Internet inter-domain routing system scalability problem (Part II). In such a way, the first known name-independent (i.e. topology unaware) compact multicast routing algorithm is proposed. On the other hand, the AnyTraffic Labeled concept is also introduced saving on forwarding entries by sharing a single forwarding entry to unicast and multicast traffic type. Exhaustive simulation campaigns are run in both cases in order to assess the reliability and feasible of the proposals

Models and solution approaches for intermodal and less-than-truckload network design with load consolidations

Logistics and supply chain problems arising in the context of intermodal transportation and less-than-truckload (LTL) network design typically require commodities to be consolidated and shipped via the most economical route to their destinations. Traditionally, these problems have been modelled using network design or hub-and- spoke approaches. In a network design problem, one is given the network and flow requirements between the origin and destination pairs (commodities), and the objective is to route the flows over the network so as to minimize the sum of the fixed charge incurred in using arcs and routing costs. However, there are possible benefits, due to economies-of-scale in transportation, that are not addressed in standard network design models. On the other hand, hub location problems are motivated by potential economies-of-scale in transportation costs when loads are consolidated and shipped together over a completely connected hub network. However, in a hub location problem, the assignment of a node to a hub is independent of the commodities originating at, or destined to, this node. Such an indiscriminate assignment may not be suitable for all commodities originating at a particular node because of their different destinations. Problems arising in the area of LTL transportation, intermodal transportation and package routing generally have characteristics such as economies- of-scale in transportation costs in addition to the requirement of commodity-based routing. Obviously, the existing network design and hub location-based models are not directly suitable for these applications. In this dissertation, we investigate the development of models and solution algorithms for problems in the areas of LTL and intermodal transportation as well as in the freight forwarders industry. We develop models and solution methods to address strategic, tactical and operational level decision issues and show computational results. This research provides new insights into these application areas and new solution methods therein. The solution algorithms developed here also contribute to the general area of discrete optimization, particularly for problems with similar characteristics

On High-Performance Benders-Decomposition-Based Exact Methods with Application to Mixed-Integer and Stochastic Problems

RÉSUMÉ : La programmation stochastique en nombres entiers (SIP) combine la difficulté de l’incertitude et de la non-convexité et constitue une catégorie de problèmes extrêmement difficiles à résoudre. La résolution efficace des problèmes SIP est d’une grande importance en raison de leur vaste applicabilité. Par conséquent, l’intérêt principal de cette dissertation porte sur les méthodes de résolution pour les SIP. Nous considérons les SIP en deux étapes et présentons plusieurs algorithmes de décomposition améliorés pour les résoudre. Notre objectif principal est de développer de nouveaux schémas de décomposition et plusieurs techniques pour améliorer les méthodes de décomposition classiques, pouvant conduire à résoudre optimalement divers problèmes SIP. Dans le premier essai de cette thèse, nous présentons une revue de littérature actualisée sur l’algorithme de décomposition de Benders. Nous fournissons une taxonomie des améliorations algorithmiques et des stratégies d’accélération de cet algorithme pour synthétiser la littérature et pour identifier les lacunes, les tendances et les directions de recherche potentielles. En outre, nous discutons de l’utilisation de la décomposition de Benders pour développer une (méta- )heuristique efficace, décrire les limites de l’algorithme classique et présenter des extensions permettant son application à un plus large éventail de problèmes. Ensuite, nous développons diverses techniques pour surmonter plusieurs des principaux inconvénients de l’algorithme de décomposition de Benders. Nous proposons l’utilisation de plans de coupe, de décomposition partielle, d’heuristiques, de coupes plus fortes, de réductions et de stratégies de démarrage à chaud pour pallier les difficultés numériques dues aux instabilités, aux inefficacités primales, aux faibles coupes d’optimalité ou de réalisabilité, et à la faible relaxation linéaire. Nous testons les stratégies proposées sur des instances de référence de problèmes de conception de réseau stochastique. Des expériences numériques illustrent l’efficacité des techniques proposées. Dans le troisième essai de cette thèse, nous proposons une nouvelle approche de décomposition appelée méthode de décomposition primale-duale. Le développement de cette méthode est fondé sur une reformulation spécifique des sous-problèmes de Benders, où des copies locales des variables maîtresses sont introduites, puis relâchées dans la fonction objective. Nous montrons que la méthode proposée atténue significativement les inefficacités primales et duales de la méthode de décomposition de Benders et qu’elle est étroitement liée à la méthode de décomposition duale lagrangienne. Les résultats de calcul sur divers problèmes SIP montrent la supériorité de cette méthode par rapport aux méthodes classiques de décomposition. Enfin, nous étudions la parallélisation de la méthode de décomposition de Benders pour étendre ses performances numériques à des instances plus larges des problèmes SIP. Les variantes parallèles disponibles de cette méthode appliquent une synchronisation rigide entre les processeurs maître et esclave. De ce fait, elles souffrent d’un important déséquilibre de charge lorsqu’elles sont appliquées aux problèmes SIP. Cela est dû à un problème maître difficile qui provoque un important déséquilibre entre processeur et charge de travail. Nous proposons une méthode Benders parallèle asynchrone dans un cadre de type branche-et-coupe. L’assouplissement des exigences de synchronisation entraine des problèmes de convergence et d’efficacité divers auxquels nous répondons en introduisant plusieurs techniques d’accélération et de recherche. Les résultats indiquent que notre algorithme atteint des taux d’accélération plus élevés que les méthodes synchronisées conventionnelles et qu’il est plus rapide de plusieurs ordres de grandeur que CPLEX 12.7.----------ABSTRACT : Stochastic integer programming (SIP) combines the difficulty of uncertainty and non-convexity, and constitutes a class of extremely challenging problems to solve. Efficiently solving SIP problems is of high importance due to their vast applicability. Therefore, the primary focus of this dissertation is on solution methods for SIPs. We consider two-stage SIPs and present several enhanced decomposition algorithms for solving them. Our main goal is to develop new decomposition schemes and several acceleration techniques to enhance the classical decomposition methods, which can lead to efficiently solving various SIP problems to optimality. In the first essay of this dissertation, we present a state-of-the-art survey of the Benders decomposition algorithm. We provide a taxonomy of the algorithmic enhancements and the acceleration strategies of this algorithm to synthesize the literature, and to identify shortcomings, trends and potential research directions. In addition, we discuss the use of Benders decomposition to develop efficient (meta-)heuristics, describe the limitations of the classical algorithm, and present extensions enabling its application to a broader range of problems. Next, we develop various techniques to overcome some of the main shortfalls of the Benders decomposition algorithm. We propose the use of cutting planes, partial decomposition, heuristics, stronger cuts, and warm-start strategies to alleviate the numerical challenges arising from instabilities, primal inefficiencies, weak optimality/feasibility cuts, and weak linear relaxation. We test the proposed strategies with benchmark instances from stochastic network design problems. Numerical experiments illustrate the computational efficiency of the proposed techniques. In the third essay of this dissertation, we propose a new and high-performance decomposition approach, called Benders dual decomposition method. The development of this method is based on a specific reformulation of the Benders subproblems, where local copies of the master variables are introduced and then priced out into the objective function. We show that the proposed method significantly alleviates the primal and dual shortfalls of the Benders decomposition method and it is closely related to the Lagrangian dual decomposition method. Computational results on various SIP problems show the superiority of this method compared to the classical decomposition methods as well as CPLEX 12.7. Finally, we study parallelization of the Benders decomposition method. The available parallel variants of this method implement a rigid synchronization among the master and slave processors. Thus, it suffers from significant load imbalance when applied to the SIP problems. This is mainly due to having a hard mixed-integer master problem that can take hours to be optimized. We thus propose an asynchronous parallel Benders method in a branchand- cut framework. However, relaxing the synchronization requirements entails convergence and various efficiency problems which we address them by introducing several acceleration techniques and search strategies. In particular, we propose the use of artificial subproblems, cut generation, cut aggregation, cut management, and cut propagation. The results indicate that our algorithm reaches higher speedup rates compared to the conventional synchronized methods and it is several orders of magnitude faster than CPLEX 12.7