Active Topology Inference using Network Coding

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

Efficient IP-level network topology capture

International audienceLarge-scale distributed traceroute-based measurement sys- tems are used to obtain the topology of the internet at the IP-level and can be used to monitor and understand the behavior of the net- work. However, existing approaches to measuring the public IPv4 net- work space often require several days to obtain a full graph, which is too slow to capture much of the network's dynamics. This paper presents a new network topology capture algorithm, NTC, which aims to bet- ter capture network dynamics through accelerated probing, reducing the probing load while maintaining good coverage. There are two novel as- pects to our approach: it focuses on obtaining the network graph rather than a full set of individual traces, and it uses past probing results in a new, adaptive, way to guide future probing. We study the performance of our algorithm on real traces and demonstrate outstanding improved performance compared to existing work.Les systèmes de mesure distribué à grande échelle basés sur l'outil Traceroute sont utilisés pour obtenir la topologie de l'internet au niveau IP et peuvent être utilisés pour surveiller et comprendre le comportement du réseau sous-jascent. Cependant, les approches existantes pour mesurer l'espace public IPv4 du réseau Internet nécessitent souvent plusieurs jours pour obtenir un graphe complet, ce qui est trop lent pour capturer une grande partie de la dynamique du réseau. Cet article présente un nouvel algorithme pour la capture de la topologie du réseau, NTC, visant à cibler la dynamique du réseau à travers l'accélération de sondage, ce qui réduit la charge de la mesure, tout en maintenant une bonne couverture. Il ya deux nouveaux aspects à notre approche: l'algorithme se concentre sur l'obtention du graphe du réseau plutôt que d'effectuer un ensemble complet de traces individuelles, et il utilise les résultats de sondage précédentes de façon à adapter la mesure et de réduire les sondes envoyées. Nous étudions les performances de notre algorithme sur des traces réelles et démontrons la performance accrue de notre approche par rapport aux travaux existants

Compact mixed integer linear programming models to the Minimum Weighted Tree Reconstruction problem

The Minimum Weighted Tree Reconstruction (MWTR) problem consists of finding a minimum length weighted tree connecting a set of terminal nodes in such a way that the length of the path between each pair of terminal nodes is greater than or equal to a given distance between the considered pair of terminal nodes. This problem has applications in several areas, namely, the inference of phylogenetic trees, the modeling of traffic networks and the analysis of internet infrastructures. In this paper, we investigate the MWTR problem and we present two compact mixed-integer linear programming models to solve the problem. Computational results using two different sets of instances, one from the phylogenetic area and another from the telecommunications area, show that the best of the two models is able to solve instances of the problem having up to 15 terminal nodes

Experiences on enhancing data collection in large networks

We improve and validate TICP, our TCPfriendly reliable transport protocol to collect information from a large number of Internet entities. A collector machine sends probes to a set of information sources that reply by sending back their reports. TICP adapts the sending rate of probes in a way similar to TCP for the purpose of avoiding network congestion and implosion at the collector. Lost reports are requested again by TICP until they are correctly received by the collector. In a first part of this work, we add to TICP a mechanism to cluster information sources in order to probe sources behind the same bottleneck together. This ensures a smooth variation of network conditions during the collection session and hence, an efficient handling of congestion at network bottlenecks. We run simulations in ns-2 over realistic topologies to compare TICP before and after clustering. We also implement the protocol in C++ and test it over the PlanetLab platform. All experiments prove the outperformance of TICP over non adaptive solutions and the interest of the clustering mechanism in shortening the duration of the collection session and in decreasing the ratio of lost packets. In a second part, we adapt TICP to collect large amounts of information from each data source. By the means of simulations, we compare the performance obtained by TICP to that obtained when the information maintained by the different sources is collected by parallel TCP connections. Again, the simulations show that TICP yields shorter collection sessions due to its inherent multiplexing capability. Finally, in a last part, we study the impact of delegating collection to some proxy sources that collect from other sources on behalf of the collector and that the collector probes later to get their collected data. We explain our method to choose the proxy collectors and we show by simulations that for a judicious choice of proxy collectors, one can decrease considerably the collection session duration

Active topology inference using network coding

Our goal, in this paper, is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work in [24]. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two source, two receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography [36], and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and other alternatives, including passive inference, traceroute, and packet marking

Topological characteristics of IP networks

Topological analysis of the Internet is needed for developments on network planning, optimal routing algorithms, failure detection measures, and understanding business models. Accurate measurement, inference and modelling techniques are fundamental to Internet topology research. A requirement towards achieving such goals is the measurements of network topologies at different levels of granularity. In this work, I start by studying techniques for inferring, modelling, and generating Internet topologies at both the router and administrative levels. I also compare the mathematical models that are used to characterise various topologies and the generation tools based on them. Many topological models have been proposed to generate Internet Autonomous System(AS) topologies. I use an extensive set of measures and innovative methodologies to compare AS topology generation models with several observed AS topologies. This analysis shows that the existing AS topology generation models fail to capture important characteristics, such as the complexity of the local interconnection structure between ASes. Furthermore, I use routing data from multiple vantage points to show that using additional measurement points significantly affect our observations about local structural properties, such as clustering and node centrality. Degree-based properties, however, are not notably affected by additional measurements locations. The shortcomings of AS topology generation models stems from an underestimation of the complexity of the connectivity in the Internet and biases of measurement techniques. An increasing number of synthetic topology generators are available, each claiming to produce representative Internet topologies. Every generator has its own parameters, allowing the user to generate topologies with different characteristics. However, there exist no clear guidelines on tuning the value of these parameters in order to obtain a topology with specific characteristics. I propose a method which allows optimal parameters of a model to be estimated for a given target topology. The optimisation is performed using the weighted spectral distribution metric, which simultaneously takes into account many the properties of a graph. In order to understand the dynamics of the Internet, I study the evolution of the AS topology over a period of seven years. To understand the structural changes in the topology, I use the weighted spectral distribution as this metric reveals differences in the hierarchical structure of two graphs. The results indicate that the Internet is changing from a strongly customer-provider oriented, disassortative network, to a soft-hierarchical, peering-oriented, assortative network. This change is indicative of evolving business relationships amongst organisations

Underlay aware approach to provide reliable and timely dissemination of events in a publish subscribe system

Publish-subscribe is a well-known paradigm for building distributed applications. Events produced by peers, called publishers, are disseminated to interested consumers, called subscribers. Usually publishers and subscribers are arranged in a peer-to-peer overlay network, which helps in dissemination of events in a decentralised manner. Recent research tries to provide Quality-of-Service like delay bounds or reliability in such a system. In order to provide reliability current distributed publish-subscribe systems mostly either rely on overlay level acknowledgement protocols or try to find multiple disjoint paths in the overlay to increase redundancy without taking into account the underlay topology. Acknowledgements induce high delays affecting timeliness of event delivery. Providing multiple paths without looking at the underlay does not take into account correlations between paths within the underlay. We address these drawbacks by designing a content-based publish-subscribe system which provides reliability by taking into account the underlay topology to reduce correlations within the underlay in overlay links. The system consists of three layers: The Topology-Discovery-Overlay (TDO) layer constructs an underlay topology aware overlay which reflects the underlay topology by using a path-matching algorithm. On top of the TDO the Maximum-Reliability-Spanning-Tree (MRST) layer constructs k overlay link disjoint trees which contain the most reliable overlay links. The MRSTs are used by the content-based publish-subscribe layer for subscription flooding and event forwarding. The system has been evaluated by simulations in PeerSim using Internet-like topologies. The results show that the TDO discovers most of the underlay topology and constructs overlay topologies reflecting the underlay topology. Simulations also show that the system converges towards a maximum event delivery probability

QoE based Management and Control for Large-scale VoD System in the Cloud

<p>The Cloud infrastructure has become an ideal platform for large-scale applications, such as Video-on-Demand (VoD). As VoD systems migrate to the Cloud, new challenges emerge. The complexity of the Cloud system due to virtualization and resource sharing complicates the Quality of Experience (QoE) management. Operational failures in the Cloud can lead to session crashes. In addition to the Cloud, there are many other systems involved in the large-scale video streaming. These systems include the Content Delivery Networks (CDNs), multiple transit networks, access networks, and user devices. Anomalies in any of these systems can affect users’ Quality of Experience (QoE). Identifying the anomalous system that causes QoE degradation is challenging for VoD providers due to their limited visibility over these systems. We propose to apply end user QoE in the management and control of large-scale VoD systems in the Cloud. We present a QoE-based management and control systems and validate them in production Clouds. QMan, a QoE based Management system for VoD in the Cloud, controls the server selection adaptively based on user QoE. QWatch, a scalable monitoring system, detects and locates anomalies based on the end-user QoE. QRank, a scalable anomaly identification system, identifies the anomalous systems causing QoE anomalies. The proposed systems are developed and evaluated in production Clouds (Microsoft Azure, Google Cloud and Amazon Web Service). QMan provides 30% more users with QoE above the “good” Mean Opinion Score (MOS) than existing server selection systems. QMan discovers operational failures by QoE based server monitoring and prevents streaming session crashes. QWatch effectively detects and locates QoE anomalies in our extensive experiments in production Clouds. We find numerous false positives and false negatives when system metric based anomaly detection methods are used. QRank identifies anomalous systems causing 99.98% of all QoE anomalies among transit networks, access networks and user devices. Our extensive experiments in production Clouds show that transit networks are the most common bottleneck causing QoE anomalies. Cloud provider should identify bottleneck transit networks and determine appropriate peering with Internet Service Providers (ISPs) to bypass these bottlenecks.</p