%0 Conference Paper %1 ghiasvand2019anomaly %A Ghiasvand, Siavash %A Ciorba, Florina M. %B 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC) %C Amsterdam, Netherlands %D 2019 %K Anomaly Bridges, Computer Correlation, Graphics HPC Hardware, Resource analysis, anomaly anonymized approach approach, architecture, components, computerised computers, computing computing, detection detection, exascale failure hardware, high hpc, instrumentation, management, mechanism, myOwn node performance prediction, privacy, processing sensors, statistical system system, units, vicinity, vicinity-based, %P 112--120 %R 10.1109/ISPDC.2019.00024 %T Anomaly Detection in High Performance Computers: A Vicinity Perspective %X 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\%.

@inproceedings{ghiasvand2019anomaly, abstract = {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\%.}, added-at = {2024-12-10T16:17:47.000+0100}, address = {Amsterdam, Netherlands}, author = {Ghiasvand, Siavash and Ciorba, Florina M.}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/27080d4de58b69911314242d735b2d79b/ghiasvan}, booktitle = {2019 18th {International} {Symposium} on {Parallel} and {Distributed} {Computing} ({ISPDC})}, doi = {10.1109/ISPDC.2019.00024}, file = {IEEE Xplore Abstract Record:D\:\\Documents\\Zotero\\storage\\EHZST9E4\\8790853.html:text/html;IEEE Xplore Full Text PDF:D\:\\Documents\\Zotero\\storage\\47Q4CND4\\Ghiasvand and Ciorba - 2019 - Anomaly Detection in High Performance Computers A.pdf:application/pdf}, interhash = {99e022cf5eb30a74956641980dd28c13}, intrahash = {7080d4de58b69911314242d735b2d79b}, keywords = {Anomaly Bridges, Computer Correlation, Graphics HPC Hardware, Resource analysis, anomaly anonymized approach approach, architecture, components, computerised computers, computing computing, detection detection, exascale failure hardware, high hpc, instrumentation, management, mechanism, myOwn node performance prediction, privacy, processing sensors, statistical system system, units, vicinity, vicinity-based,}, month = jun, note = {ISSN: 2379-5352}, pages = {112--120}, shorttitle = {Anomaly {Detection} in {High} {Performance} {Computers}}, timestamp = {2024-12-10T16:31:07.000+0100}, title = {Anomaly {Detection} in {High} {Performance} {Computers}: {A} {Vicinity} {Perspective}}, year = 2019 }