Khan, Junaid Ahmed
(2023)
Graph Neural Networks for Anomaly Anticipation in HPC Systems.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Ingegneria elettronica [LM-DM270], Documento ad accesso riservato.
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Abstract
This thesis investigates the potential of Graph Neural Networks (GNNs) for anticipating anomalies in high performance computing (HPC) systems. The proposed approach is based on the structure of the HPC system, which involves compute nodes in close proximity to each other. This is possible due to GNNs. GNNs learn
to represent each node in the graph by aggregating information from its neighbors, allowing them to capture the graph’s topology and structural information. The task is framed as a supervised prediction problem, where the GNN predicts the probability of a compute node failing within a fixed-length future window. Experiments were carried out on a production Tier-0 supercomputer hosted at CINECA datacenter facilities, the largest Italian provider of HPC, and they demonstrated promising results in anomaly prediction, especially when tested on real, production data.
Abstract
This thesis investigates the potential of Graph Neural Networks (GNNs) for anticipating anomalies in high performance computing (HPC) systems. The proposed approach is based on the structure of the HPC system, which involves compute nodes in close proximity to each other. This is possible due to GNNs. GNNs learn
to represent each node in the graph by aggregating information from its neighbors, allowing them to capture the graph’s topology and structural information. The task is framed as a supervised prediction problem, where the GNN predicts the probability of a compute node failing within a fixed-length future window. Experiments were carried out on a production Tier-0 supercomputer hosted at CINECA datacenter facilities, the largest Italian provider of HPC, and they demonstrated promising results in anomaly prediction, especially when tested on real, production data.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Khan, Junaid Ahmed
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
CURRICULUM ELECTRONICS FOR INTELLIGENT SYSTEMS, BIG-DATA AND INTERNET OF THINGS
Ordinamento Cds
DM270
Parole chiave
AI,Anomaly prediction,GNN,Machine Learning,HPC Systems
Data di discussione della Tesi
19 Luglio 2023
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Khan, Junaid Ahmed
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
CURRICULUM ELECTRONICS FOR INTELLIGENT SYSTEMS, BIG-DATA AND INTERNET OF THINGS
Ordinamento Cds
DM270
Parole chiave
AI,Anomaly prediction,GNN,Machine Learning,HPC Systems
Data di discussione della Tesi
19 Luglio 2023
URI
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