This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical analysis of Young and Daly in the presence of a fault prediction system, which is characterized by its recall and its precision, and which provides either exact or window-based time predictions. We succeed in deriving the optimal value of the checkpointing period (thereby minimizing the waste of resource usage due to checkpoint overhead) in all scenarios. These results allow to analytically assess the key parameters that impact the performance of fault predictors at very large scale. In addition, the results of this analytical evaluation are nicely corroborated by a comprehensive set of simulations, thereby demonstrating the validity of the model and the accuracy of the results.
Considerable research has been conducted on fault prediction using different models (system log analysis @cite_13 , event-driven approach @cite_9 @cite_13 @cite_0 , support vector machines @cite_6 @cite_11 ), nearest neighbors @cite_6 , ). In this section we give a brief overview of the results obtained by predictors. We focus on their results rather than on their methods of prediction.
@cite_9 @cite_6 @cite_0 @cite_13 @cite_11
2094924503 2040352153 2141676918 130470821
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. Analyzing, understanding and predicting failure is of paramount importance to achieve effective fault management. While various fault prediction methods have been studied in the past, many of them are not practical for use in real systems. In particular, they fail to address two crucial issues: one is to provide location information (i.e., the components where the failure is expected to occur on) and the other is to provide sufficient lead time (i.e., the time interval preceding the time of failure occurrence). In this paper, we first refine the widely-used metrics for evaluating prediction accuracy by including location as well as lead time. We, then, present a practical failure prediction mechanism for IBM Blue Gene systems. A Genetic Algorithm based method is exploited, which takes into consideration the location and the lead time for failure prediction. We demonstrate the effectiveness of this mechanism by means of real failure logs and job logs collected from the IBM Blue Gene P system at Argonne National Laboratory. Our experiments show that the presented method can significantly improve fault management (e.g., to reduce service unit loss by up to 52.4 ) by incorporating location and lead time information in the prediction. To facilitate proactive fault management in large-scale systems such as IBM Blue Gene P, online failure prediction is of paramount importance. While many techniques have been presented for online failure prediction, questions arise regarding two commonly used approaches: period-based and event-driven. Which one has better accuracy? What is the best observation window (i.e., the time interval used to collect evidence before making a prediction)? How does the lead time (i.e., the time interval from the prediction to the failure occurrence) impact prediction arruracy? To answer these questions, we analyze and compare period-based and event-driven prediction approaches via a Bayesian prediction model. We evaluate these prediction approaches, under a variety of testing parameters, by means of RAS logs collected from a production supercomputer at Argonne National Laboratory. Experimental results show that the period-based Bayesian model and the event-driven Bayesian model can achieve up to 65.0 and 83.8 prediction accuracy, respectively. Furthermore, our sensitivity study indicates that the event-driven approach seems more suitable for proactive fault management in large-scale systems like Blue Gene P. Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure and limit the impact. Such strategies are especially important for multi-computer systems, such as compute clusters, that experience a higher rate failure due to the large number of components. However providing accurate predictions with sufficient lead time remains a challenging problem. This paper describes a new spectrum-kernel Support Vector Machine (SVM) approach to predict failure events based on system log files. These files containmessages that represent a change of system state. While a single message in the file may not be sufficient for predicting failure, a sequence or pattern of messages may be. The approach described in this paper will use a sliding window (sub-sequence) of messages to predict the likelihood of failure. The a frequency representation of the message sub-sequences observed are then used as input to the SVM. The SVM then associates the messages to a class of failed or non-failed system. Experimental results using actual system log files from a Linux-based compute cluster indicate the proposed spectrum-kernel SVM approach has promise and can predict hard disk failure with an accuracy of 73 two days in advance.