In this paper we study the problem of predictability in partially observable discrete event systems, i.e., the question whether an observer can predict the occurrence of a fault. We extend the definition of predictability to consider the time interval where the fault will occur: the @math -predictability does not only specify that the fault will be predicted before it occurs, but also that the predictor will be able to predict that its occurrence will occur in @math to @math observations from now. We also provide a quadratic algorithm that decides predictability of the system.
Brand 'an Briones and Madalinski presented the notions of @math -predictability and @math -predictability @cite_15 . @math -predictability is similar to our definition of @math -predictability meaning that the fault is predicted at least @math observations before the fault occurs. @math -predictability is the equivalent of our property of @math -predictability, meaning that it is possible to predict the fault occurrence before it occurs but when at most @math observations are still possible before the fault (in other words, the fault prediction is not too early).
@cite_15
2329723054 2151666086 2344277659 1455915656
A discrete event system that is predictable can anticipate faults and act accordingly. However, bare predictability (as found in the literature) has some drawbacks. One does not know exactly when a fault occurs (it could be immediately or long after it is predicted). We propose lower and upper bound refinements of predictability, called (lb; ub)-predictability. A lower bound guarantees a fault to occur after certain execution steps, whereas an upper bound guarantees a fault to occur, in the future, but before some steps. This information can be exploited by the system to adopt the best contingency plan. We formally define the notions of lb-predictability and ub-predictability, present a decision algorithm to verify it on arbitrary systems, and illustrate them with examples. Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively. We consider the problem of identifying the source of failure in a network after receiving alarms or having observed symptoms. To locate the root cause accurately and timely in a large communication system is challenging because a single fault can often result in a large number of alarms, and multiple faults can occur concurrently. In this paper, we present a new fault localization method using a machine-learning approach. We propose to use logistic regression to study the correlation among network events based on end-to-end measurements. Then based on the regression model, we develop fault hypothesis that best explains the observed symptoms. Unlike previous work, the machine-learning algorithm requires neither the knowledge of dependencies among network events, nor the probabilities of faults, nor the conditional probabilities of fault propagation as input. The “low requirement” feature makes it suitable for large complex networks where accurate dependencies and prior probabilities are difficult to obtain. We then evaluate the performance of the learning algorithm with respect to the accuracy of fault hypothesis and the concentration property. Experimental results and theoretical analysis both show satisfactory performance. The exponential growth of computational power of the extreme scale machines over the past few decades has led to a corresponding decrease in reliability and a sharp increase of the frequency of hardware faults. Our research focuses on the mathematical challenges presented by the silent hardware faults; i.e., faults that can perturb the result of computations in an inconspicuous way. Using the approach of selective reliability, we present an analytic fault mode that can be used to study the resilience properties of a numerical algorithm. We apply our approach to the classical fixed point iteration and demonstrate that in the presence of hardware faults, the classical method fails to converge in expectation. We preset a modified resilient algorithm that detects and rejects faults resulting in error with large magnitude, while small faults are negated by the natural self-correcting properties of the algorithm. We show that our method is convergent (in first and second statistical moments) even in the presenc...