Abstract Increase in the network usage for more and more performance critical applications has caused a demand for tools that can monitor network health with minimum management traffic. Adaptive probing has the potential to provide effective tools for end-to-end monitoring and fault diagnosis over a network. Adaptive probing based algorithms adapt the probe set to localize faults in the network by sending less probes in healthy areas and more probes in the suspected areas of failure. In this paper we present adaptive probing tools that meet the requirements to provide an effective and efficient solution for fault diagnosis for modern communication systems. We present a system architecture for adaptive probing based fault diagnosis tool and propose algorithms for probe selection to perform failure detection and fault localization. We compare the performance and efficiency of the proposed algorithms through simulation results.

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Developing measurement tools that can concurrently monitor congested Internet links at a large scale would significantly help us understand how the Internet operates. While congestion at the Internet edge typically arises due to bottlenecks existent at a connection’s last mile, congestion in the core could be more complex. This is because it may depend upon internal network policies and hence can reveal systematic problems such as routing pathologies, poorly-engineered network policies, or non-cooperative inter-AS relationships. Therefore, enabling the tools to provide deeper insights about congestion in the core is certainly beneficial. In this paper, we present the design and implementation of a large-scale triggered monitoring system that focuses on monitoring a subset of Internet core links that exhibit relatively strong and persistent congestion, i.e., hot spots. The system exploits triggered mechanisms to address its scalability; moreover, it automates selection of good vantage points to handle the common measurement experience that the much more congested Internet edges could often overshadow the observation for congestion in the core. Using the system, we characterize the properties of concurrently monitored hot spots. Contrary to common belief, we find that strong time-invariant hot spots reside in the Internet core — both within and between large backbone networks. Moreover, we find that congestion events at these hot spots can be highly correlated and such correlated congestion events can span across up to three neighboring ASes. We provide a root-cause analysis to explain this phenomenon and discuss implications of our findings. This work is supported by a Cisco Collaborative Research grant.

Quality of service (QoS) support for multimedia applications in large-scale networks

Modeling the available bandwidth of a path using a known stochastic process is one possible method for esti-mating future available bandwidth along the path without explicit support from network routers. Our two hypotheses for the stochastic process are as follows. First, an auto-regressive integrated moving-average process (ARIMA) is a suitable model for the available bandwidth over time of a path. Second, the available bandwidth over time of a path can be modeled as a self-similar process. We verify both hypotheses using R statistical software and available bandwidth data sets published by Stanford Linear Acceler-ator Center (SLAC). Our results indicate that the available bandwidth over time of an end-to-end path can be mod-eled as fractional Gaussian Noise (FGN) and seasonal frac-tional ARIMA (SFARIMA) processes. On the other hand, we found that an ARIMA process is not a good model for avail-able bandwidth over time of an end-to-end path. 1

Continuous monitoring and diagnosis of network performance are of crucial importance for the Internet access service and virtual private network (VPN) service providers. Various operational constraints, which are crucial to the practice, are largely ignored in previous monitoring system designs, or are simply replaced with load balancing problems which do not work for real heterogeneous networks. Given these real-world challenges, in this paper, we design a V Scope monitoring system with the following contributions. First, we design a greedy-assisted linear programming algorithm to select as few monitors as possible that can monitor the whole network under the operational constraints. Secondly, VScope takes a multiround measurement approach which gives a smooth tradeoff between measurement frequency and monitors deployment/management cost. We propose three algorithms to schedule the path measurements in different rounds obeying the operational constraints. Finally, we design a continuous monitoring and diagnosis mechanism which selects the minimal extra paths to measure to identify the faulty links after the discovery of faulty paths. Evaluations based on several real VPN topologies from a tier-1 ISP as well as some other synthetic topologies demonstrate that VScope is promising to solve the aforementioned challenges.

Génie de la constmction à l'École de technologie supérieure M. Stanislaw Kajl, codirecteur de mémoire Génie mécanique à l'École de technologie supérieure M. Stéphane Halle, président du jury Génie mécanique à l'École de technologie supérieure M. Radu Zmeureanu, examinateur externe Génie du bâtiment à l'Université Concordia