Diagnosing the root-causes of failures from cluster log files

Chuah, Edward; Kuo, Shyh-hao; Hiew, Paul; Tjhi, William-Chandra; Lee, Gary; Hammond, John L.; Michalewicz, Marek T.; Hung, Terence; Browne, James C.

doi:10.1109/hipc.2010.5713159

Cited by 37 publications

(40 citation statements)

References 16 publications

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“…Most of the current research uses a pre-definite time window for which rules are investigated and extracted [7,8,9,10,13]. However a fix interval for all event types and for the whole life of the system is not realistic.…”

Section: Introductionmentioning

confidence: 99%

Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

Gainaru¹,

Cappello

Fullop³

et al. 2011

Managing Large-Scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques

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In this paper, we analyse messages generated by different HPC large-scale systems in order to extract sequences of correlated events which we lately use to predict the normal and faulty behaviour of the system. Our method uses a dynamic window strategy that is able to find frequent sequences of events regardless on the time delay between them. Most of the current related research narrows the correlation extraction to fixed and relatively small time windows that do not reflect the whole behaviour of the system. The generated events are in constant change during the lifetime of the machine. We consider that it is important to update the sequences at runtime by applying modifications after each prediction phase according to the forecast's accuracy and the difference between what was expected and what really happened. Our experiments show that our analysing system is able to predict around 60% of events with a precision of around 85% at a lower event granularity than before.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

Gainaru¹,

Cappello

Fullop³

et al. 2011

Managing Large-Scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques

View full text Add to dashboard Cite

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“…Unsupervised techniques require only a few input parameters, the rest being done automatically by the algorithm. The most used unsupervised methods for extracting information from log files are the Apriori algorithm for frequent itemsets [17,19,18], Latent Semantic Indexing [9], event pattern [10,4] and k-nearest neighbours [23]. Also, there are some studies that use the source code to extract error description format [6].…”

Section: Related Workmentioning

confidence: 99%

“…Message Template Extractor (MTE) is a component contained by the FDiag tool [4] that adds structure to the logs by extracting the messages template. The main idea used by the authors is that tokens in the English dictionary show the same patterns in different messages from the same type and that alpha-numerical values do not have the same property.…”

Section: Related Workmentioning

confidence: 99%

“…We have made experiments in order to compare our tool with two Apriori tools (Loghound [17], SLCT [19]), two other pattern extractors (IPLOM [10], MTE [4]), and an affinity propagation technique (StrAp [23]). HELO outperforms all other algorithms providing a better precision without an overhead in the computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…The learned patterns may not be applicable for a long time so all analyzing techniques must be able to detect phase shifts in behavior. Current data mining algorithms [17,19,10,4,15,14] have difficulties in dynamically updating the groups to cope with an incoming stream of novel events. In this paper we present HELO (Hierarchical Event Log Organizer) a novel unsupervised clustering engine that aims to accurately mine event type patterns from log files generated by large supercomputers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event Log Mining Tool for Large Scale HPC Systems

Gainaru

Cappello

Trăușan-Matu

et al. 2011

Euro-Par 2011 Parallel Processing

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Abstract. Event log files are the most common source of information for the characterization of events in large scale systems. However the large size of these files makes the task of manual analysing log messages to be difficult and error prone. This is the reason why recent research has been focusing on creating algorithms for automatically analysing these log files. In this paper we present a novel methodology for extracting templates that describe event formats from large datasets presenting an intuitive and user-friendly output to system administrators. Our algorithm is able to keep up with the rapidly changing environments by adapting the clusters to the incoming stream of events. For testing our tool, we have chosen 5 log files that have different formats and that challenge different aspects in the clustering task. The experiments show that our tool outperforms all other algorithms in all tested scenarios achieving an average precision and recall of 0.9, increasing the correct number of groups by a factor of 1.5 and decreasing the number of false positives and negatives by an average factor of 4.

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An Approach to Failure Prediction in Cluster by Self-updating Cause-and-Effect Graph

Yan

Chen

2019

Cloud Computing – CLOUD 2019

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Diagnosing the root-causes of failures from cluster log files

Cited by 37 publications

References 16 publications

Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

Event Log Mining Tool for Large Scale HPC Systems

An Approach to Failure Prediction in Cluster by Self-updating Cause-and-Effect Graph

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