Multi-level anomaly prediction in Tier-0 datacenter
Published: 17 May 2022
Proceedings of the 19th ACM International Conference on Computing Frontiers ; https://doi.org/10.1145/3528416.3530864
Abstract: Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. To solve large problems in science, engineering, and business, data centers provide High-Performance Computing (HPC) systems with aggregation of the computing capacity of thousand of computing nodes. Anomaly prediction is critical in order to preserve the continuity of the service of HPC systems and prevent hardware deterioration. In the datacenter, a thermal anomaly occurs when the balance of cooling capacity and computational demand is disturbed. Moreover, this is identifiable from a suspicious/abnormal pattern in the monitoring signals. In this poster, the anomaly prediction task in the HPC systems is investigated by defining complex statistical rules-based and Deep Learning DL-based anomaly detection methods, then utilizing these anomaly detection methods in an anomaly prediction framework.
Keywords: anomaly prediction / HPC systems / deep learning / datacenter
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Click here to see the statistics on "Proceedings of the 19th ACM International Conference on Computing Frontiers" .