development of self-healing systems capable of making inferences about their own behavior, such as diagnosing faults and performance degradations. uses a cost-efficient technique for adaptive diagnosis that combines probabilistic inference with online, active selection of the most-informative measurements called probes. Probes are end-to-end test transactions that collect information about the availability and performance of a distributed system. Given the probe results (symptoms), RAIL performs Bayesian inference in order to find the most likely explanation (cause), An important difference between RAIL's approach and ''passive'' data analysis is in RAIL's ability to select and execute probes online. This approach, called active probing, uses an information-theoretic criterion called information gain in order to select adaptively only a small set of the most informative probes at any given time; this approach significantly reduces the overall number of probes required

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