The heterogeneity of inter-contact time distributions: its importance for routing in delay tolerant networks

Prior work on routing in delay tolerant networks (DTNs) has commonly made the assumption that each pair of nodes shares the same inter-contact time distribution as every other pair. The main argument in this paper is that researchers should also be looking at heterogeneous inter-contact time distributions. We demonstrate the presence of such heterogeneity in the often-used Dartmouth Wi-Fi data set. We also show that DTN routing can benefit from knowing these distributions. We first introduce a new stochastic model focusing on the inter-contact time distributions between all pairs of nodes, which we validate on real connectivity patterns. We then analytically derive the mean delivery time for a bundle of information traversing the network for simple single copy routing schemes. The purpose is to examine the theoretic impact of heterogeneous inter-contact time distributions. Finally, we show that we can exploit this user diversity to improve routing performance.Comment: 6 page

Evaluating Mobility Pattern Space Routing for DTNs

Because a delay tolerant network (DTN) can often be partitioned, the problem of routing is very challenging. However, routing benefits considerably if one can take advantage of knowledge concerning node mobility. This paper addresses this problem with a generic algorithm based on the use of a high-dimensional Euclidean space, that we call MobySpace, constructed upon nodes' mobility patterns. We provide here an analysis and the large scale evaluation of this routing scheme in the context of ambient networking by replaying real mobility traces. The specific MobySpace evaluated is based on the frequency of visit of nodes for each possible location. We show that the MobySpace can achieve good performance compared to that of the other algorithms we implemented, especially when we perform routing on the nodes that have a high connection time. We determine that the degree of homogeneity of mobility patterns of nodes has a high impact on routing. And finally, we study the ability of nodes to learn their own mobility patterns.Comment: IEEE INFOCOM 2006 preprin

Adaptive Robust Traffic Engineering in Software Defined Networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201

MakeSense: Managing Reproducible WSNs Experiments

International audienceWireless Sensor Networks (WSN) users often use simulation campaigns before real deployment to evaluate performance and to fine- tune application and network parameters. This process requires repeating the same experiments under similar conditions and to collect, parse and present data efficiently. This paper introduces MakeSense: a tool that automates this workflow and that allows reproducing simulations easily by defining the whole experiment and post-processing steps in a single JSON configuration file, easy to share and to modify. MakeSense also provides interfaces to interact with a running simulation, allowing to send external stimuli and to collect data in real time. MakeSense currently runs over the COOJA simulator, but has been built to be easily adapted to other architectures, including real testbeds

Modèle de propagation opportuniste pour soulager l'infrastructure 3G

International audienceNous étudions la diffusion massive de contenus en déplaçant une partie du trafic du réseau d'infrastructure 3G vers un réseau opportuniste sans pour autant sacrifier la fiabilité. Dans ce papier, nous étudions un modèle de propagation de messages dans un réseau opportuniste afin de pouvoir prédire l'évolution de la prochaine propagation et calculer le nombre optimal de copies à injecter dans le réseau au début d'une diffusion

Fair Coflow Scheduling via Controlled Slowdown

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the σ-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers