Bernard Cousin

Bio: Bernard Cousin is an academic researcher from University of Rennes. The author has contributed to research in topics: Multicast & Source-specific multicast. The author has an hindex of 20, co-authored 136 publications receiving 1239 citations. Previous affiliations of Bernard Cousin include Institut de Recherche en Informatique et Systèmes Aléatoires & University of Rennes 1.

Resource saving: Which resource sharing strategy to protect primary shortest paths?

Abstract: Two strategies of resource sharing are proposed in literature to provide protection while saving resources: (1) restrained sharing which applies the resource sharing to the backup paths only and (2) global sharing which extends the resource sharing to the primary and backup paths. In this paper, we compared the two strategies of resource sharing when the primary paths correspond to the shortest ones according to a strictly positive and static metric. Even when the amount of resources that can be shared between the primary and the backup paths is unbounded, we proved that the maximum number of backup paths is still bounded. Besides, our simulations showed that the resource sharing between the primary and backup paths has very slight impact on the backup path rejection, i.e. the two strategies of resource sharing have very close performances.

Localization of Single Link-Level Network Anomalies

Abstract: Achieving accurate, cost-efficient, and fast anomaly localization is a highly desired feature in computer networks. Prior works, examining the problem of single link-level anomaly localization, have claimed that a necessary condition for localizing anomalies unambiguously is to deploy resources that enable the monitoring of a set of paths distinguishing between all links of the network pairwise. In this paper, we show that the number of pair of links that are to be distinguished can be cut down drastically using an already established anomaly detection solution. This results in reducing the localization overhead and cost significantly. Furthermore, we show that all potential anomaly scenarios can be derived offline from the anomaly detection solution. Therefore, we compute full localization solutions, i.e. monitors that are to be activated and paths that are to be monitored, for all potential anomaly scenarios offline. This results in a significant minimization of the localization delay. We devise an anomaly localization technique that selects monitor locations and monitoring paths jointly; thereby enabling a trade-off between the number and locations of monitoring devices and the quality of monitoring paths. The problem is formulated as an integer linear program (ILP), and is shown to be NP-hard through a polynomial-time reduction from the NP-hard facility location problem. The effectiveness and the correctness of the proposed anomaly localization scheme are verified through theoretical analysis and extensive simulations.

Candidate-cycle-based heuristic algorithm for node-and-link protection of dynamic multicast traffic in optical DWDM networks

Abstract: Maintaining survivability of DWDM networks is crucial to multicast traffic. A link-or-node failure has a severe impact on optical multicast sessions as it can prune several communications simultaneously. In this paper, we present a novel candidate-cycle-based heuristic algorithm for node-and-link protection (CCHN) in dynamic multicast traffic. CCHN is based on p-cycle protection concept. The p-cycle concept ensures a fast restoration time and an efficient use of network capacity. Extensive simulations show that the blocking probability of our algorithm is lowest. Furthermore, the computational time of our algorithm is very low compared with the existing approaches, especially when traffic load is high.

A Hybrid End-to-End QoS Path Computation Algorithm for PCE-Based Multi-Domain Networks

Abstract: Inter-domain quality of service (QoS) routing is a challenging problem for today's Internet. This problem requires the computation of paths that cross multiple domains and meet different QoS constraints. In addition, the used computation methods must meet the constraints of confidentiality and autonomy imposed by the domains of different operators. Path computation element (PCE)-based architecture offers a promising solution for inter-domain QoS routing. It ensures the computation of end-to-end QoS paths while preserving the confidentiality and the autonomy of the domains. In this paper, we propose a novel hybrid end-to-end QoS path computation algorithm, named HID-MCP, for PCE-based networks. HID-MCP is a hybrid algorithm that combines the advantages of pre-computation and on-demand computation to obtain end-to-end QoS paths. Moreover, it integrates a crankback mechanism for improving path computation results in a single domain or in multiple domains based on the PCE architecture. Detailed analyses are provided to assess the performance of our algorithm in terms of success rate and computational complexity. The simulation results show that our algorithm has an acceptance rate of the requests very close to the optimal solution; precisely, the difference is lower than 1 % in a realistic network. Moreover, HID-MCP has a low computational complexity. Besides, our solution relies on the PCE architecture to overcome the limitations related to inter-domain routing such as domain autonomy and confidentiality.

Optical burst switching (OBS) - a new paradigm for an optical Internet

Abstract: To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Abstract: In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Abstract: Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.