Frequent failures 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 develop effective fault-tolerant strategies, there is a critical need to predict failure events. To this end, we have collected detailed event logs from IBM BlueGene/L, which has 128 K processors, and is currently the fastest supercomputer in the world. In this study, we first show how the event records can be converted into a data set that is appropriate for running classification techniques. Then we apply classifiers on the data, including RIPPER (a rule-based classifier), Support Vector Machines (SVMs), a traditional Nearest Neighbor method, and a customized Nearest Neighbor method. We show that the customized nearest neighbor approach can outperform RIPPER and SVMs in terms of both coverage and precision. The results suggest that the customized nearest neighbor approach can be used to alleviate the impact of failures.

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.

The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM’s BlueGene/L which can accommodate as many as 128K processors. One of the challenges when designing and deploying these systems in a production setting is the need to take failure occurrences, whether it be in the hardware or in the software, into account. Ear- lier work has shown that conventional runtime fault- tolerant techniques such as periodic checkpointing are not effective to the emerging systems. Instead, the ability to predict failure occurrences can help develop more effective checkpointing strategies. Failure predic- tion has long been regarded as a challenging research problem, mainly due to the lack of realistic failure data from actual production systems. In this study, we have collected RAS event logs from BlueGene/L over a pe- riod of more than 100 days. We have investigated the characteristics of fatal failure events, as well as the correlation between fatal events and non-fatal events. Based on the observations, we have developed three simple yet effective failure prediction methods, which can predict around 80% of the memory and network failures, and 47% of the application I/O failures.

The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L, which can accommodate as many as 128K processors. In this paper, we present our experiences in collecting and filtering error event logs from a 8192 processor BlueGene/L prototype at IBM Rochester, which is currently ranked #8 in the Top-500 list. We analyze the logs collected from this machine over a period of 84 days starting from August 26, 2004. We perform a three-step filtering algorithm on these logs: extracting and categorizing failure events; temporal filtering to remove duplicate reports from the same location; and finally coalescing failure reports of the same error across different locations. Using this approach, we can substantially compress these logs, removing over 99.96% of the 828,387 original entries, and more accurately portray the failure occurrences on this system.