References of "Leduc, Guy"
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See detailRating Network Paths for Locality-Aware Overlay Construction and Routing
Du, Wei; Liao, Yongjun ULg; Tao, Narisu et al

in IEEE/ACM Transactions on Networking (2014)

This paper investigates the rating of network paths, i.e. acquiring quantized measures of path properties such as round-trip time and available bandwidth. Comparing to finegrained measurements, coarse ... [more ▼]

This paper investigates the rating of network paths, i.e. acquiring quantized measures of path properties such as round-trip time and available bandwidth. Comparing to finegrained measurements, coarse-grained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we firstly address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements. [less ▲]

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See detailDMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
Liao, Yongjun ULg; Du, Wei; Geurts, Pierre ULg et al

in IEEE/ACM Transactions on Networking (2013), 21(5), 1511-1524

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a ... [more ▼]

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the prediction problem as matrix completion where the unknown entries in a pairwise distance matrix constructed from a network are to be predicted. By assuming that the distance matrix has a low-rank characteristics, the problem is solvable by lowrank approximation based on matrix factorization. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach, but also its usability in real Internet applications. [less ▲]

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See detailTowards a Standards-Based Cloud Service Manager
Ghrab, Amine; Skhiri, Sabri; Koener, Hervé et al

Poster (2013, May)

Migrating services to the cloud brings all the benefits of elasticity, scalability and cost-cutting. However, migrating services among different cloud infrastructures or outside of the cloud is not an ... [more ▼]

Migrating services to the cloud brings all the benefits of elasticity, scalability and cost-cutting. However, migrating services among different cloud infrastructures or outside of the cloud is not an obvious task. In addition, distributing services among multiple cloud providers, or on a hybrid installation requires a custom implementation effort that must be repeated at each infrastructure change. This situation raises the lock-in problem and discourages cloud adoption. Cloud computing open standards were designed to face this situation and to bring interoperability and portability to cloud environments. However, they target isolated resources, and do not take into account the notion of complete services. In this paper, we introduce an extension to OCCI, a cloud computing open standard, in order to support complete service definition and management automation. We support this proposal with an open-source framework for service management through compliant cloud infrastructures. [less ▲]

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See detailOutbound SPIT Filter with Optimal Performance Guarantees
Jung, Tobias ULg; Martin, Sylvain ULg; Nassar, Mohamed et al

in Computer Networks (2013), 57(7), 16301643

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely ... [more ▼]

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely different from related previous work: our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering an abstracted scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error (false positive and false negative), apply Wald’s sequential probability ratio test to the individual sources, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks. [less ▲]

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See detailOrdinal Rating of Network Performance and Inference by Matrix Completion
Du, Wei; Liao, Yongjun ULg; Geurts, Pierre ULg et al

Report (2012)

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former ... [more ▼]

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former reduces measurement costs and unifies various metrics which eases their processing in applications. The latter enables scalable and accurate inference with no requirement of structural information of the network nor geometric constraints. By combining both, the acquisition problem bears strong similarities to recommender systems. This paper investigates the applicability of various matrix factorization models used in recommender systems. We found that the simple regularized matrix factorization is not only practical but also produces accurate results that are beneficial for peer selection. [less ▲]

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See detailContextual Multi-armed Bandits for the Prevention of Spam in VoIP Networks
Jung, Tobias ULg; Martin, Sylvain ULg; Ernst, Damien ULg et al

E-print/Working paper (2012)

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two ... [more ▼]

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual one and an algorithmical one. The conceptual contribution is to formulate -- as an example -- the real-world problem of preventing SPIT (Spam in VoIP networks), which is currently not satisfyingly addressed by standard techniques, as a sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning SPIT filter that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first simulation results. [less ▲]

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See detailDISco: a Distributed Information Store for Network Challenges and Their Outcome
Martin, Sylvain ULg; Chiarello, Laurent ULg; Leduc, Guy ULg

in Keeney, John; Serrat, Joan (Eds.) 5th International workshop on Distributed Autonomous Network Management Systems (2012, April)

We present the design of DISco, a storage and communication middleware that enables distributed and task-centric autonomic control of networks. DISco allows multi-agent identification of anomalous ... [more ▼]

We present the design of DISco, a storage and communication middleware that enables distributed and task-centric autonomic control of networks. DISco allows multi-agent identification of anomalous situations (challenges) and assists coordinated remediation that will maintain service at an acceptable level, although degraded. The history of agents decisions, their context and outcomes is tracked as the situation evolves, and information is automatically gathered and organised to ease further human-assisted diagnosis. We then explore the feasibility of using state of the art peer-to-peer publish/subscribe and storage systems as building blocks for this service. The ability of those systems to support range queries and aggregation will be a key factor for their suitability to the task. [less ▲]

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See detailEditorial for Computer Networks special issue on “Measurement-based optimization of P2P networking and applications”
Fu, Xiaoming; Chen, Yang; Leduc, Guy ULg et al

in Computer Networks (2012), 26(3), 1077-1079

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See detailDISco: a Distributed Information Store for network Challenges and their Outcome
Martin, Sylvain ULg; Chiarello, Laurent ULg; Leduc, Guy ULg

Report (2012)

We present DISco, a storage and communication middleware designed to enable distributed and task-centric autonomic control of networks. DISco is designed to enable multi-agent identification of anomalous ... [more ▼]

We present DISco, a storage and communication middleware designed to enable distributed and task-centric autonomic control of networks. DISco is designed to enable multi-agent identification of anomalous situations -- so-called "challenges" -- and assist coordinated remediation that maintains degraded -- but acceptable -- service level, while keeping a track of the challenge evolution in order to enable human-assisted diagnosis of flaws in the network. We propose to use state-of-art peer-to-peer publish/subscribe and distributed storage as core building blocks for the DISco service. [less ▲]

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See detailDMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
Liao, Yongjun ULg; Du, Wei; Geurts, Pierre ULg et al

Report (2012)

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a ... [more ▼]

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the distance prediction problem as matrix completion where unknown entries of an incomplete matrix of pairwise distances are to be predicted. The problem is solvable because strong correlations among network distances exist and cause the constructed distance matrix to be low rank. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed to solve the network distance prediction problem. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach but also its usability in real Internet applications. [less ▲]

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See detailSPRT for SPIT: Using the Sequential Probability Ratio Test for Spam in VoIP Prevention
Jung, Tobias ULg; Martin, Sylvain ULg; Ernst, Damien ULg et al

in Proc. of 6th International Conference on Autonomous Infrastructure, Management and Security (2012)

This paper presents the first formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work ... [more ▼]

This paper presents the first formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work deviates from related earlier work where this problem is only addressed by ad-hoc solutions. Our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering a scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error, apply Wald’s sequential probability ratio test, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks. [less ▲]

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See detailContextual Multi-armed Bandits for Web Server Defense
Jung, Tobias ULg; Martin, Sylvain ULg; Ernst, Damien ULg et al

in Hussein, Abbas (Ed.) Proceedings of 2012 International Joint Conference on Neural Networks (IJCNN) (2012)

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two ... [more ▼]

In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual and an algorithmical one. The conceptual contribution is to formulate the real-world problem of preventing HTTP-based attacks on web servers as a one-shot sequential learning problem, namely as a contextual multi-armed bandit. Our second contribution is to present CMABFAS, a new algorithm for general contextual multi-armed bandit learning that specifically targets domains with finite actions. We illustrate how CMABFAS could be used to design a fully self-learning meta filter for web servers that does not rely on feedback from the end-user (i.e., does not require labeled data) and report first convincing simulation results. [less ▲]

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See detailDecentralized Prediction of End-to-End Network Performance Classes
Liao, Yongjun ULg; Du, Wei; Geurts, Pierre ULg et al

in Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT) (2011, December 08)

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we ... [more ▼]

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers. In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection. [less ▲]

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See detailUsing Decision Trees for Generating Adaptive SPIT Signatures
Nassar, Mohamed Ali; Martin, Sylvain ULg; Leduc, Guy ULg et al

in Proc. of the 4th International Conference on Security of Information and Networks (SIN 2011) (2011, November 14)

With the spread of new and innovative Internet services such as SIP-based communications, the challenge of protecting and defending these critical applications has been raised. In particular, SIP ... [more ▼]

With the spread of new and innovative Internet services such as SIP-based communications, the challenge of protecting and defending these critical applications has been raised. In particular, SIP firewalls attempt to filter the signaling unwanted activities and attacks based on the knowledge of the SIP protocol. Optimizing the SIP firewall configuration at real-time by selecting the best filtering rules is problematic because it depends on both natures of the legal traffic and the unwanted activities. More precisely, we do not know exactly how the unwanted activities are reflected in the SIP messages and in what they differ from the legal ones. In this paper, we address the case of Spam over Internet Telephony (SPIT) mitigation. We propose an adaptive solution based on extracting signatures from learnt decision trees. Our simulations show that quickly learning the optimal configuration for a SIP firewall leads to reduce at lowest the unsolicited calls as reported by the users under protection. Our results promote the application of machine learning algorithms for supporting network and service resilience against such new challenges. [less ▲]

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See detailFinding Routing Shortcuts using an Internet Coordinate System
Cantin, François ULg; Leduc, Guy ULg

in Lecture Notes in Computer Science (2011, February 23), 6557

Overlay routing is a promising way to improve the quality of service in the Internet but its main drawback is scalability: measuring the characteristics of the paths, exchanging the measurement results ... [more ▼]

Overlay routing is a promising way to improve the quality of service in the Internet but its main drawback is scalability: measuring the characteristics of the paths, exchanging the measurement results between the nodes and computing the best routes in the full mesh overlay network generally imply a high consumption of resources. In this paper, we design the basis of a lightweight self-organising one-hop overlay routing mechanism improving the latencies: we define criteria that rely on the information provided by an Internet Coordinate System (ICS) in order to provide a small set of potential one-hop shortcuts for any given path in the network with a small measurement cost. Our best criterion does not guarantee to find the best shortcut for any given path in a network but, even in networks with hundreds or thousands of nodes, it will restrict the search for potential shortcuts to about one or two percent of the total number of nodes. [less ▲]

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See detailNetwork Distance Prediction Based on Decentralized Matrix Factorization
Liao, Yongjun ULg; Geurts, Pierre ULg; Leduc, Guy ULg

in Lecture Notes in Computer Science (2010, May 11), 6091

Network Coordinate Systems (NCS) are promising techniques to predict unknown network distances from a limited number of measurements. Most NCS algorithms are based on metric space embedding and suffer ... [more ▼]

Network Coordinate Systems (NCS) are promising techniques to predict unknown network distances from a limited number of measurements. Most NCS algorithms are based on metric space embedding and suffer from the inability to represent distance asymmetries and Triangle Inequality Violations (TIVs). To overcome these drawbacks, we formulate the problem of network distance prediction as guessing the missing elements of a distance matrix and solve it by matrix factorization. A distinct feature of our approach, called Decentralized Matrix Factorization (DMF), is that it is fully decentralized. The factorization of the incomplete distance matrix is collaboratively and iteratively done at all nodes with each node retrieving only a small number of distance measurements. There are no special nodes such as landmarks nor a central node where the distance measurements are collected and stored. We compare DMF with two popular NCS algorithms: Vivaldi and IDES. The former is based on metric space embedding, while the latter is also based on matrix factorization but uses landmarks. Experimental results show thatDMF achieves competitive accuracy with the double advantage of having no landmarks and of being able to represent distance asymmetries and TIVs. [less ▲]

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See detailEnhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning
El Khayat, Ibtissam; Geurts, Pierre ULg; Leduc, Guy ULg

in Wireless Networks (2010), 16(2), 273-290

TCP is suboptimal in heterogeneous wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. In this paper, we propose to improve TCP performance ... [more ▼]

TCP is suboptimal in heterogeneous wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. In this paper, we propose to improve TCP performance in wired/wireless networks by endowing it with a classifier that can distinguish packet loss causes. In contrast to other proposals we do not change TCP’s congestion control nor TCP’s error recovery. A packet loss whose cause is classified as link error will simply be ignored by TCP’s congestion control and recovered as usual, while a packet loss classified as congestion loss will trigger both mechanisms as usual. To build our classification algorithm, a database of pre-classified losses is gathered by simulating a large set of random network conditions, and classification models are automatically built from this database by using supervised learning methods. Several learning algorithms are compared for this task. Our simulations of different scenarios show that adding such a classifier to TCP can improve the throughput of TCP substantially in wired/wireless networks without compromizing TCP-friendliness in both wired and wireless environments. [less ▲]

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See detailResolving the Noxious Effect of Churn on Internet Coordinate Systems
Gueye, Cheikh Ahmadou Bamba ULg; Leduc, Guy ULg

in Lectures Notes in Computer Science (2009, December), 5918

Internet Coordinate Systems (ICS) provide easy and practical latency predictions in the Internet. However, peer dynamics (i.e, churn), which is an in- herent property of peer-to-peer (P2P) systems ... [more ▼]

Internet Coordinate Systems (ICS) provide easy and practical latency predictions in the Internet. However, peer dynamics (i.e, churn), which is an in- herent property of peer-to-peer (P2P) systems, affects the accuracy of such sys- tems. This paper addresses the problem of churn in an ICS without landmarks, like Vivaldi. We propose a framework to assess the robustness of such an ICS in the presence of churn, and evaluate two models for handling churn. The key idea is to reactively recover lost neighbours, either by picking new nodes at random, or by selecting a new one among the node’s two-hop neighbours, while maintaining high reliability and low communication overhead. We then show by simulations that our models mitigate the impact of churn, and lead to a good accuracy com- pared to an instance of an ICS running without churn. [less ▲]

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See detailTransformation non linéaire des distances : une solution au problème des violations des inégalités triangulaires dans les systèmes de coordonnées ?
Cantin, François ULg; Leduc, Guy ULg; Gueye, Cheikh Ahmadou Bamba ULg

in CFIP'2009 (2009, October 12)

Network coordinate systems embed delay measurements (e.g. RTT) between Internet nodes into some metric space. It is well known that triangle inequality violations by the measured delays are a source of ... [more ▼]

Network coordinate systems embed delay measurements (e.g. RTT) between Internet nodes into some metric space. It is well known that triangle inequality violations by the measured delays are a source of inaccuracy for coordinate systems. A solution to this problem has been proposed in [wan08]. The idea is to apply a non linear transformation to the measured delays in order to obtain delays which respect the triangle inequality. With this approach we can expect to obtain an estimated delay matrix that contains the triangule inequalities of the measured matrix. Such result is really interesting but we will show in this paper that the results are not as good as hoped. By using simulations, we have observed that the estimated delay matrices obtained by using non linear transformations of the delays are more accurate than what is usually obtained with a coordinate system. However, despite what [wan08] lets hope, it seems really difficult to obtain an estimated delay matrix that contains the same triangle inequality violations than the measured delay matrix. [less ▲]

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See detailOptimisation des débits des couches d'une transmission vidéo multipoint avec une meilleure prise en compte du surcoût d'encodage
Soldani, Cyril ULg; Leduc, Guy ULg

in CFIP'2009 (2009, October 12)

In video stream transmission, one often proposes to solve the problem of the heterogeneity of the receivers by dividing the stream into several cumulative layers distributed over multicast transmission. A ... [more ▼]

In video stream transmission, one often proposes to solve the problem of the heterogeneity of the receivers by dividing the stream into several cumulative layers distributed over multicast transmission. A dynamic adaptation by the source of the number and size of layer bit-rates to the set of its receivers allows a better usage of the network resources. Efficient algorithms have been designed to do this adaptation. However, the encoding of a video stream in layers incurs an overhead relatively to the single-layer encoding of the same stream. A too naive modeling of this overhead causes a suboptimal allocation of bit-rate. We propose an improvement of the layer bit-rate adaptation algorithm by dynamic programming to take a finer overhead model and other encoder constraints into account. To this end, we introduce a more general overhead model. As our algorithm is of high complexity, we also present a heuristic allowing to find quickly a solution which is near the optimum and we test this heuristic by simulations based on our model. [less ▲]

Detailed reference viewed: 45 (13 ULg)