Enabling Dependability-Driven Resource Use and Message Log-Analysis for Cluster System Diagnosis

Abstract

Recent work have used both failure logs and resource use data separately (and together) to detect system failureinducing errors and to diagnose system failures. System failure occurs as a result of error propagation and the (unsuccessful) execution of error recovery mechanisms. Knowledge of error propagation patterns and unsuccessful error recovery is important for more accurate and detailed failure diagnosis, and knowledge of recovery protocols deployment is important for improving system reliability. This paper presents the CORRMEXT framework which carries failure diagnosis another significant step forward by analyzing and reporting error propagation patterns and degrees of success and failure of error recovery protocols. CORRMEXT uses both error messages and resource use data in its analyses. Application of CORRMEXT to data from the Ranger supercomputer have produced new insights. CORRMEXT has: (i) identified correlations between resource use counters that capture recovery attempts after an error, (ii) identified correlations between error events to capture error propagation patterns within the system, (iii) identified error propagation and recovery paths during system execution to explain system behaviour, (iv) showed that the earliest times of change in system behaviour can only be identified by analyzing both the correlated resource use counters and correlated errors. CORRMEXT will be installed on the HPC clusters at the Texas Advanced Computing Center in Autumn 2017.

Sentiment Analysis based Error Detection for Large-Scale Systems

“…[12], [18]- [20]) and many have been evaluated on different benchmark logs' datasets in [21]. Moreover, various tools and much research have been dedicated to diagnosing the root causes of failures such as [22]- [24].…”

Failure Detection Using Semantic Analysis and Attention-Based Classifier Model for IT Infrastructure Log Data

“…Therefore, it is required to follow the action of text data conversion into numerical vectors. In the existing literature, researchers applied indexed-based methods [19] [20] [21] or semantic-based methods [22][23] [24] to extract the features of logs. In the index-based extraction, log data is converted to log template indexes, and afterward, sequential or quantitative features are extracted against the generated indexes.…”