Miao Jiang

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Correlations among management metrics in software systems allow errors to be detected and their cause localized. Prior research shows that linear models can capture many of these correlations. However, our research shows that several factors may prevent linear models from accurately describing correlations, even if the underlying relationship is linear. Two(More)
Stable correlation models are effective in detecting errors in complex software systems. However, most studies assume a specific mathematical form, typically linear, for the underlying correlations. In practice, more complex non-linear relationships exist between metrics. Moreover, most inter-metric correlations form clusters rather than simple pairwise(More)
Ensuring high availability, adequate performance, and proper operation of enterprise software systems requires continuous monitoring. Today, most systems operate with minimal monitoring, typically based on service-level objectives (SLOs). Detailed metric-based monitoring is often too costly to use in production, while tracing is prohibitively expensive.(More)
Enterprise software systems (ESS) are becoming larger and increasingly complex. Failure in business-critical systems is expensive, leading to consequences such as loss of critical data, loss of sales, customer dissatisfaction, even law suits. Therefore, detecting failures and diagnosing their root-cause in a timely manner is essential. Many studies suggest(More)