Optimizing probe selection for fault localization

We investigate the use of probing technology for the purpose of problem determination and fault localization in networks. We present a framework for addressing this issue and implement algorithms that exploit interactions between probe paths to find a small collection of probes that can be used to locate faults. Small probe sets are desirable in order to minimize the costs imposed by probing, such as additional network load and data management requirements. Our results show that although finding the optimal collection of probes is expensive for large networks, efficient approximation algorithms can be used to find a nearly-optimal set

Efficient Probing Techniques for Fault Diagnosis

Abstract — Increase in the network usage and the widespread application of networks for more and more performance critical applications has caused a demand for tools that can monitor network health with minimum management traffic. Adaptive probing holds a potential to provide effective tools for end-toend monitoring and fault diagnosis over a network. In this paper we present adaptive probing tools that meet the requirements to provide an effective and efficient solution for fault diagnosis. In this paper, we propose adaptive probing based algorithms to perform fault localization by adapting the probe set to localize the faults in the network. We compare the performance and efficiency of the proposed algorithms through simulation results

EFFICIENT PROBE STATION PLACEMENT AND PROBE SET SELECTION FOR FAULT LOCALIZATION

Network fault management has been a focus of research activity with more emphasis on fault localization – zero down exact source of a failure from set of observed failures. Fault diagnosis is a central aspect of network fault management. Since faults are unavoidable in communication systems, their quick detection and isolation is essential for the robustness, reliability, and accessibility of a system. Probing technique for fault localization involves placement of probe stations (Probe stations are specially instrumented nodes from where probes can be sent to monitor the network) which affects the diagnosis capability of the probes sent by the probe stations. Probe station locations affect probing efficiency, monitoring capability, and deployment cost. We present probe station selection algorithms and aim to minimize the number of probe stations and make the monitoring robust against failures in a deterministic as well as a non-deterministic environment. We then implement algorithms that exploit interactions between probe paths to find a small collection of probes that can be used to locate faults. Small probe sets are desirable in order to minimize the costs imposed by probing, such as additional network load and data management requirements. We discuss a novel integrated approach of probe station and probe set selection for fault localization. A better placing of probe stations would produce fewer probes and probe set maintaining same diagnostic power. We provide experimental evaluation of the proposed algorithms through simulation results

deTector: a Topology-aware Monitoring System for Data Center Networks

Troubleshooting network performance issues is a challenging task especially in large-scale data center networks. This paper presents deTector, a network monitoring system that is able to detect and localize network failures (manifested mainly by packet losses) accurately in near real time while minimizing the monitoring overhead. deTector achieves this goal by tightly coupling detection and localization and carefully selecting probe paths so that packet losses can be localized only according to end-to-end observations without the help of additional tools (e.g., tracert). In particular, we quantify the desirable properties of the matrix of probe paths, i.e., coverage and identifiability, and leverage an efficient greedy algorithm with a good approximation ratio and fast speed to select probe paths. We also propose a loss localization method according to loss patterns in a data center network. Our algorithm analysis, experimental evaluation on a Fattree testbed and supplementary large-scale simulation validate the scalability, feasibility and effectiveness of deTector.published_or_final_versio

Are Aftershocks of Large Californian Earthquakes Diffusing?

We analyze 21 aftershock sequences of California to test for evidence of space-time diffusion. Aftershock diffusion may result from stress diffusion and is also predicted by any mechanism of stress weakening. Here, we test an alternative mechanism to explain aftershock diffusion, based on multiple cascades of triggering. In order to characterize aftershock diffusion, we develop two methods, one based on a suitable time and space windowing, the other using a wavelet transform adapted to the removal of background seismicity. Both methods confirm that diffusion of seismic activity is very weak, much weaker than reported in previous studies. A possible mechanism explaining the weakness of observed diffusion is the effect of geometry, including the localization of aftershocks on a fractal fault network and the impact of extended rupture lengths which control the typical distances of interaction between earthquakes.Comment: latex file of 34 pages, 15 postscript figures, minor revision. In press in J. Geophys. Re

Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis

Erfassung und Evaluierung von Teilentladungen in Leistungstransformatoren mit speziellen Sensoren und Diagnoseverfahren

Transformers are key elements of the power grid. Due to their importance and high initial cost, asset managers utilize monitoring and diagnostic tools to optimize their operation and extend their service life. The main objective of this thesis is to develop new methods in the field of monitoring and diagnosis of transformers in order to reduce maintenance costs and decrease the frequency of forced outages. For this purpose, two concepts are proposed. Small generator step-up transformers are essential in wind and photovoltaic parks. The first presented concept entails an online fault gas monitoring system for these transformers, specially hermetically-sealed transformers. The developed compact, maintenance-free and cost-effective monitoring system continuously tracks the level of the key leading indicators of transformer faults in the gas cushion. The second presented concept revolves around partial discharge (PD) assessment by the UHF measurement technique, which is based on capturing the electromagnetic (EM) waves emitted in case of PD in the insulation of a transformer. In this context, the complex EM system established when probes are introduced into the tank of a transformer and with PD as the excitation source is analyzed. Drawing on this foundation, a practical approach to the detection and classification of PD with the focus on the selection of the optimal frequency range for performing UHF measurements depending on the device under test is presented. The UHF measurement technique also offers the possibility of PD localization. Here, the determined arrival time (AT) of the captured signals is critical. A PD localization algorithm, based on a multi-data-set approach with a novel AT determination method, is proposed. The methods and algorithms proposed for the detection, classification and localization of PD are validated by means of practical experiments

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions