A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log

Park, Byung H.; Hui, Yawei; Boehm, Swen; Ashraf, Rizwan A.; Layton, Christopher; Engelmann, Christian

doi:10.1109/cluster.2018.00073

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…Furthermore, the resource allocation vicinity will be further studied via analyzing jobs' information that are executed across multiple nodes. Performing similar analyses on publicly available system logs, such as those from the Failure Trace Archive (FTA) 5 or the Computer Failure Data Repository (CFDR) 6 , as well as other types of monitoring data, such as node power consumption, are also planned as future work.…”

Section: Discussionmentioning

confidence: 99%

“…For example, points A : (1, 10) and B : (4, 6) in a 2D Cartesian representation are separated by a distance of 4 − 1 = 3 on the X axis and 10−6 = 4 on the Y axis, respectively. Defining the new dimension Z, according to a common (but so far unseen) feature of A and B would result in a 3D representation of A : (1, 10, 5) and B : (4,6,5). Here '5' denotes that common feature.…”

Section: A Vicinitiesmentioning

confidence: 99%

“…Anomaly detection is a well-known general purpose approach for detecting failures in computing systems [4]. In HPC systems, system log analysis can be used for anomaly detection for the purpose of preventing failures [5]- [8]. All HPC systems on the current TOP500 [9] list are Linux-based.…”

Section: Introductionmentioning

confidence: 99%

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Anomaly Detection in High Performance Computers: A Vicinity Perspective

Ghiasvand

Ciorba

2019

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)

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In response to the demand for higher computational power, the number of computing nodes in high performance computers (HPC) increases rapidly. Exascale HPC systems are expected to arrive by 2020. With drastic increase in the number of HPC system components, it is expected to observe a sudden increase in the number of failures which, consequently, poses a threat to the continuous operation of the HPC systems. Detecting failures as early as possible and, ideally, predicting them, is a necessary step to avoid interruptions in HPC systems operation. Anomaly detection is a well-known general purpose approach for failure detection, in computing systems. The majority of existing methods are designed for specific architectures, require adjustments on the computing systems hardware and software, need excessive information, or pose a threat to users' and systems' privacy. This work proposes a node failure detection mechanism based on a vicinity-based statistical anomaly detection approach using passively collected and anonymized system log entries. Application of the proposed approach on system logs collected over 8 months indicates an anomaly detection precision between 62% to 81%.

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Section: Discussionmentioning

confidence: 99%

Section: A Vicinitiesmentioning

confidence: 99%

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Anomaly Detection in High Performance Computers: A Vicinity Perspective

Ghiasvand

Ciorba

2019

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)

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“…In [45], the authors integrated probabilistic analysis with an optimized K-means algorithm to detect error propagation across the nodes in a HPC system. In [46], the authors presented a big-data analytics framework that mines event patterns and provides user application and system event correlations. In [47], the authors presented a scalable, intuitive HPC data analysis framework.…”

Section: Related Workmentioning

confidence: 99%

Failure Diagnosis for Cluster Systems using Partial Correlations

Chuah¹,

Jhumka²,

Alt³

et al. 2021

2021 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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Failures have expensive implications in HPC (High-Performance Computing) systems. Consequently, effective diagnosis of system failures is desired to help improve system reliability from both a remedial and preventive perspective. As HPC systems conduct extensive logging of resource usage and system events, parsing this data is an oft advocated basis for failure diagnosis. However, the high levels of concurrency that exist in HPC systems cause system events to frequently interleave in time and, as such, certain interactions appear or become indirect. which will be missed by current failure diagnostics techniques. To help uncover such indirect interactions, in this paper, we develop a novel approach that leverages the concept of partial correlation. The novel failure diagnostics workflow -called IFADE -extracts partial correlation of resource use counters and partial correlation of system errors. As part of our contributions, we (a) compare our diagnostics approach with current ones, (b) identify two previously unknown causes of system failures, validated by system designers and (c) provide insights into Lustre I/O and segmentation faults. IFADE has been put on the public domain to support system administrators in failure diagnosis.

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“…More existing literature of user authentication techniques and mechanisms used in distributed HPC systems can be found in [4,14,28]. 5 EAI As machine learning has been widely spread into different domains, a more direct and effective strategy has come into people's mind to defend HPC systems, log file-based defense [24,29,30], which is a behavior detection method based on log files generated from running jobs. Using log files has many advantages than previous methods, as log files are a detailed record of the running process of the applications.…”

Section: Intrusion Detectionmentioning

confidence: 99%

Security of HPC Systems: From a Log-analyzing Perspective

Luo¹,

Qu²,

Zeng³

et al. 2019

ICST Transactions on Security and Safety

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High Performance Computing (HPC) systems mainly focused on how to improve performances of the computing. It has competitive processing capacity both in terms of calculation speed and available memory. HPC infrastructures are valuable computing resources that need to be carefully guarded and avoid being maliciously used. Thus, vulnerabilities are quintessential issues in HPC systems due to most of jobs and resources run or stored usually are sensitive and high-profit information. In this survey, we comprehensively review securities of HPC systems from a log-analyzing perspective, including well-known attacks and widely used defenses, especially intruder detection methods. We found that log files are used for the security purposes much less than what we expected. How to use all the available log files comprehensively and employ state-ofthe-art intrusion techniques to improve the robustness of HPC systems still lies for future research.

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A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log

Cited by 13 publications

References 19 publications

Anomaly Detection in High Performance Computers: A Vicinity Perspective

Anomaly Detection in High Performance Computers: A Vicinity Perspective

Failure Diagnosis for Cluster Systems using Partial Correlations

Security of HPC Systems: From a Log-analyzing Perspective

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