In order to achieve data delivery in Delay Tolerant Networks (DTN), researchers have proposed the use of store-carry-and-forward protocols: a node there may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. This way, messages can traverse disconnected parts of the network. Multiple message replicas are often propagated to further increase delivery probability. This combination of long-term storage and message replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and drop policies are necessary to 1) decide on the order by which messages should be replicated when contact durations are limited, and 2) which messages should be discarded when nodes' buffers operate close to their capacity. In this paper, we propose a practical and efficient joint scheduling and drop policy that can optimize different performance metrics, such as average delay and delivery probability. We first use the theory of encounter-based message dissemination to derive the optimal policy based on global knowledge about the network. Then, we introduce a method that estimates all necessary parameters using locally collected statistics. Based on this, we derive a distributed scheduling and drop policy that can approximate the performance of the optimal policy in practice. Using simulations based on synthetic and real mobility traces, we show that our optimal policy and its distributed variant outperform existing resource allocation schemes for DTNs. Finally, we study how sampled statistics can reduce the signaling overhead of our algorithm and examine its behavior under different congestion regimes. Our results suggest that close to optimal performance can be achieved even when nodes sample a small percentage of the available statistics.

The remarkable growth of the Internet infrastructure and the increasing heterogeneity of applications and users' behavior make more complex the manageability and monitoring of ISP networks and raises the cost of any new deployment. The main consequence of this trend is an inherent disagreement between existing monitoring solutions and the increasing needs of management applications. In this context, we present the design of an adaptive centralized architecture that provides visibility over the entire network through a network-wide cognitive monitoring system. Practically, given a measurement task and a constraint on the volume of collected information, the proposed architecture drives the sampling rates on the interfaces of network routers to achieve the maximum possible accuracy, while adapting itself to any change in network traffic conditions. We illustrate our work with an accounting application whose purpose is to estimate the volume of aggregate flows across a backbone transit network. The paper provides a global study of the functioning of the proposed system and the impact of the different parameters on its behavior. The performance of our system is validated in typical scenarios over an experimental platform we developed for the purpose of the study.

Ubiquity of portable computing devices coupled with wide availability of wireless communication present new impor- tant opportunities for applications involving media-rich content dissemination. However, as access networks become increasingly more heterogeneous, seamless data delivery across internets consisting of a variety of network technology becomes a real challenge. In this demonstration, we showcase a system that enables content dissemination over heterogeneous internets consisting of wired, infrastructure-based and infrastructure-less wireless networks that may be prone to intermittent connectivity. Using an efficient, yet flexible buffer management scheme, we are able to address application-specific performance requirements such as average delay, delivery probability, energy efficiency, etc. Our system uses the Message Delivery in Heterogeneous, Disruption-prone Networks (MeDeHa) [2]) framework to deliver messages across a heterogeneous internet coupled with History-Based Scheduling and Drop (HBSD) buffer management [1] as a way to optimize resources provided by opportunistic networks. MeDeHa, which is described in detail in [2], provides seamless data delivery over interconnecting networks of different types, i.e., infrastructure-based and infrastructure-less networks. MeDeHa's comprehensive approach to bridging infrastructure-based and infrastructureless networks also copes with intermittent connectivity. For this demonstration, we showcase a "complete stack" solution featuring, from to top to bottom, the DTN2 "bundle" layer, HBSD as an "external router" to DTN2, and MeDeHa, which handles message delivery. We have implemented, on a Linux-based testbed, (i) the MeDeHa framework, (ii) the HBSD [3] external router for the DTN2 [4] architecture.

It is of utmost importance for the network research community to have access to tools and testbeds to explore future directions for Internet traffic monitoring and engineering. Although many experimental solutions exist today, they tend to be highly specialized or to have a limited availability and openness. Through this work, we outline the monitoring capabilities limitations of these facilities and we present our emulation platform for network wide traffic monitoring as an answer to these limitations. Our platform presents a new approach for the emulation of Internet traffic and for its monitoring across the different routers. Through our solution, we put at the disposal of users a real traffic emulation service coupled to a set of libraries and tools capable of Cisco NetFlow data export and collection, the overall destined to run advanced applications for network wide traffic monitoring and optimization.

As a result of the wide spread of new efficient mobile communication devices, users are more and more willing to find on these devices (PDAs, Smart-phones, etc) applications similar to the ones they are using on their desktop computers. We go one step in this direction and propose in this demo a software we have designed and implemented to enable content sharing among spontaneous communities of mobile users using wireless multihop connections. Our contribution consists in designing, implementing and testing a tracker-less Bit-Torrent like application adapted to wireless ad-hoc networks. The proposed architecture includes two principal components: a membership management service and a content sharing service. As classical tracker-based Bit-Torrent membership management and peer discovery are unfeasible in ad hoc networks, we design the membership management service as a distributed tracker overlay that connects peers involved in the same sharing session. Using the membership information provided by the tracker overlay, the content sharing service schedules the data transfer connections among the session members by leveraging the multihop routing feature of wireless ad-hoc networks. Our test-bed composed of PDAs and smart-phones equipped with WIFI adapters and Windows Mobile 6 operating system is used to validate the feasibility of our application and to evaluate its performance.

Delay Tolerant Networks are wireless networks where disconnections may occur frequently due to propagation phenomena, node mobility, and power outages. Propagation delays may also be long due to the operational environment (e.g. deep space, underwater). In order to achieve data delivery in such challenging networking environments, researchers have proposed the use of store-carry-and-forward protocols: there, a node may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. Additionally, multiple message replicas are often propagated to increase delivery probability. This combination of long-term storage and replication imposes a high storage overhead on untethered nodes (e.g. handhelds). Thus, efficient buffer management policies are necessary to decide which messages should be discarded, when node buffers are operated close to their capacity. In this paper, we propose efficient buffer management policies for delay tolerant networks. We show that traditional buffer management policies like drop-tail or drop-front fail to consider all relevant information in this context and are, thus, sub-optimal. Using the theory of encounter-based message dissemination, we propose an optimal buffer management policy based on global knowledge about the network. Our policy can be tuned either to minimize the average delivery delay or to maximize the average delivery rate. Finally, we introduce a distributed algorithm that uses statistical learning to approximate the global knowledge required by the the optimal algorithm, in practice. Using simulations based on a synthetic mobility model and real mobility traces, we show that our buffer management policy based on statistical learning successfully approximates the performance of the optimal policy in all considered scenarios. At the same time, our policy outperforms existing ones in terms of both average delivery rate and delivery delay.

Delay tolerant networks (DTN) are wireless networks where disconnections may occur frequently. In order to achieve data delivery in DTNs, researchers have proposed the use of store-carry-and-forward protocols: there, a node may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. Multiple message replicas are often propagated to increase delivery probability. This combination of long-term storage and replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and drop policies are necessary to: (i) decide on the order by which messages should be replicated when contact durations are limited, and (ii) which messages should be discarded when nodespsila buffers operate close to their capacity. In this paper, we propose an efficient joint scheduling and drop policy that can optimize different performance metrics, like average delay and delivery probability. Using the theory of encounter-based message dissemination, we first propose an optimal policy based on global knowledge about the network. Then, we introduce a distributed algorithm that can approximate the performance of the optimal algorithm, in practice. Using simulations based on a synthetic mobility model and a real mobility trace, we show that our optimal policy and its distributed variant outperform existing resource allocation schemes for DTNs, such as the RAPID protocol [4], both in terms of average delivery ratio and delivery delay.