43,630 research outputs found
Towards Distributed and Adaptive Detection and Localisation of Network Faults
We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95 % of the generated faults can be detected with few false alarms. At least 80 % of the link faults and 65 % of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages
Network Identification for Diffusively-Coupled Systems with Minimal Time Complexity
The theory of network identification, namely identifying the (weighted)
interaction topology among a known number of agents, has been widely developed
for linear agents. However, the theory for nonlinear agents using probing
inputs is less developed and relies on dynamics linearization. We use global
convergence properties of the network, which can be assured using passivity
theory, to present a network identification method for nonlinear agents. We do
so by linearizing the steady-state equations rather than the dynamics,
achieving a sub-cubic time algorithm for network identification. We also study
the problem of network identification from a complexity theory standpoint,
showing that the presented algorithms are optimal in terms of time complexity.
We also demonstrate the presented algorithm in two case studies.Comment: 12 pages, 3 figure
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Knowing the largest rate at which data can be sent on an end-to-end path such
that the egress rate is equal to the ingress rate with high probability can be
very practical when choosing transmission rates in video streaming or selecting
peers in peer-to-peer applications. We introduce probabilistic available
bandwidth, which is defined in terms of ingress rates and egress rates of
traffic on a path, rather than in terms of capacity and utilization of the
constituent links of the path like the standard available bandwidth metric. In
this paper, we describe a distributed algorithm, based on a probabilistic
graphical model and Bayesian active learning, for simultaneously estimating the
probabilistic available bandwidth of multiple paths through a network. Our
procedure exploits the fact that each packet train provides information not
only about the path it traverses, but also about any path that shares a link
with the monitored path. Simulations and PlanetLab experiments indicate that
this process can dramatically reduce the number of probes required to generate
accurate estimates
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