One of the great challenges in Internet service fault management under noisy and uncertain environment lies in the difficulty of fault priori distribution acquisition. To address the problem, an active probing based approach is proposed for the Internet service in this paper. A hidden Markov model(HMM) based dynamic probabilistic dependency model is chosen to be the fault propagation model (FPM). A forward-backward(F-B) learning procedure is employed for the estimation of FPM. F-B fully takes both uncertainty and excessive probing traffic load into account, revising the FPM with active probing and online learning techniques. Detection probes and diagnosis probes were employed separately in fault detection phase and fault diagnosis phase. The selection of diagnosis probes is integrated into the online model learning procedure. As for fault diagnosis, a Viterbi N-best based approach is proposed to record N most likely faulty components, utilizing the probing information gain in the F-B learning procedure. As a result it can reduce the complexity of the fault priori distribution acquisition, further enhancing the accuracy of the detection rate. Simulation results prove the validity and efficiency of the HMM-based FPM model and proposed approaches.Index Terms-fault diagnosis, hidden Markov model, forward-backward learning, Viterbi N-best inference, active probing
In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, challenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases: fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm (GAPSA), which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm (FDPSA), which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic (FPTS). Simulation results prove the validity and efficiency of our approach. service management, fault management, active probing, bipartite Bayesian network, binary symmetric channel
The third-person effect hypothesis has become one of the most important aspects in the research field within the American empirical school. A large number of studies have adopted empirical research methods to verify the reliability of the third-person effect. With the rise of the network society, local research on the third-person effect has gradually extended to the verification or falsification of the third-person effect in the network environment. This article begins with a study on the third-person effect of online commercial advertisements based on the students from Guangzhou Huashang College. Through the study, the research hypotheses have been proposed and questionnaires have been distributed to the research subjects for analysis. Based on a series of quantitative operations, such as data analysis, empirical observations, and empirical research, this study provides a source of reference and reflection for research in this field.
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