An Adaptive Semantic Filter for Blue Gene/L Failure Log Analysis

Frequent failure occurrences are becoming a serious concern to the community of high-end computing, especially when the applications and the underlying systems rapidly grow in size and complexity. In order to better understand the failure behavior of such systems and further develop effective fault-tolerant strategies, we have collected detailed event logs from IBM Blue Gene/L, which has as many as 128K processors, and is currently the fastest supercomputer in the world. Due to the scale of such machines and the granularity of the logging mechanisms, the logs can get voluminous and usually contain records which may not all be distinct. Consequently, it is crucial to filter these logs towards isolating the specific failures, which can then be useful for subsequent analysis. However, existing filtering methods either require too much domain expertise, or produce erroneous results. This paper thus fills this crucial void by designing and developing an Adaptive Semantic Filtering (ASF) method, which is accurate, light-weight, and more importantly, easy to automate. Specifically, ASF exploits the semantic correlation between two events, and dynamically adapts the correlation threshold based on the temporal gap between the events. We have validated the ASF method using the failure logs collected from Blue Gene/L over a period of 98 days. Our experimental results show that ASF can effectively remove redundant entries in the logs, and the filtering results can serve as a good base for future failure analysis studies.