Name
Abstract | ||
|---|---|---|
Reliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution. During operation, several types of fault conditions or anomalies can arise, ranging from malfunctioning hardware to improper configurations or imperfect software. Currently, system administrator and final users have to discover it manually. Clearly this approach does not scale to large scale supercomputers and facilities: automated methods to detect faults and unhealthy conditions is needed. Our method uses a type of neural network called autoencoder trained to learn the normal behavior of a real, in-production HPC system and it is deployed on the edge of each computing node. We obtain a very good accuracy (values ranging between 90% and 95%) and we also demonstrate that the approach can be deployed on the supercomputer nodes without negatively affecting the computing units performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/AICAS.2019.8771527 | 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) |
Keywords | Field | DocType |
Anomaly Detection,HPC,BeagleBoneBlack,Autoencoders,Semi-supervised Learning,Edge Computing | Anomaly detection,Supercomputer,Computer science,Real-time computing,Ranging,Software,System administrator,Artificial neural network,Distributed computing | Journal |
Volume | ISBN | Citations |
abs/1902.08447 | 978-1-5386-7885-5 | 1 |
PageRank | References | Authors |
0.38 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Borghesi | 1 | 17 | 2.06 |
| A. Libri | 2 | 2 | 3.09 |
| Luca Benini | 3 | 13116 | 1188.49 |
| Andrea Bartolini | 4 | 457 | 51.90 |