Abstract
Log data is a valuable resource for failure prediction and troubleshooting in large-scale systems. However, with the rapid growth of the system scale and the popularity of various applications in productional environments, the volume of logs emerged per day becomes huge, posing serious challenges for storage and analysis. To solve these problems, we propose an online log filtering mechanism to eliminate the redundant and noisy log records through event filtering and instance filtering, aiming to minimize the log size without losing important information required for the fault diagnosis. Our proposed log filtering is evaluated on a real log data derived from a productional cloud computing system, observing that over 76% of the storage space are saved without losing important information.
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