Fault Diagnosis Based on Evidences Screening in Virtual Network

Abstract-Network virtualization has been regarded as a core attribute of Future Internet. To improve the quality of virtual network, it is important to diagnose the faulty components quickly and accurately. Recently more and more researches focus on end-user fault diagnosis, which can fit incomplete knowledge and dynamic challenges. In this paper, we present a fault diagnosis system called DiaEO in virtual network. It improves the present end-user fault diagnosis methods by screening evidences before analyzing to reduce the time-consuming. Besides that, DiaEO also improves the anti-noise ability of the system. The simulation results show that the proposed method can keep high accuracy and ameliorate time performance

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate

Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches. Objective: To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring. Method: We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results: We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft

A S. Probabilistic fault diagnosis using adaptive probing

Abstract. Past research on probing-based network monitoring provides solutions based on preplanned probing which is computationally expensive, is less accurate, and involves a large management traffic. Unlike preplanned probing, adaptive probing proposes to select probes in an interactive manner sending more probes to diagnose the observed problem areas and less probes in the healthy areas, thereby significantly reducing the number of probes required. Another limitation of most of the work proposed in the past is that it assumes a deterministic dependency information between the probes and the network components. Such an assumption can not be made when complete and accurate network information might not be available. Hence, there is a need to develop network monitoring algorithms that can localize failures in the network even in the presence of uncertainty in the inferred dependencies between probes and network components. In this paper, we propose a fault diagnosis tool with following novel features: (1) We present an adaptive probing based solution for fault diagnosis which is cost-effective, failure resistant, more accurate, and involves less management traffic as compared to the preplanned probing approach. (2) We address the issues that arise with the presence of a non-deterministic environment and present probing algorithms that consider the involved uncertainties in the collected network information.