This dataset entry showcases a comprehensive collection obtained from the Tier-0 supercomputer, Marconi A2, hosted at CINECA (https://www.hpc.cineca.it/). The dataset records inlet and outlet temperatures along with power consumption data from 3312 computing nodes, spanning from January 14, 2019, to December 31, 2019. The data is generated through ExaMon, a sophisticated monitoring datacenter infrastructure. The primary objective of this dataset is to support the research and development of HazardNet, an innovative thermal hazard prediction framework tailored specifically for datacenters. HazardNet integrates a comprehensive pipeline of machine-learning models. Researchers and enthusiasts interested in exploring our work further can find the complete set of codes and machine-learning models at our GitHub repository: https://github.com/MSKazemi/HazardNet
Mohsen Seyedkazemi Ardebili, Andrea ACQUAVIVA, LUCA BENINI, Andrea Bartolini (2023). Dataset of the HazardNet: A Thermal Hazard Prediction Framework for Datacenters [10.5281/zenodo.10050368].
Dataset of the HazardNet: A Thermal Hazard Prediction Framework for Datacenters
Mohsen Seyedkazemi Ardebili
;Andrea ACQUAVIVA;LUCA BENINI;Andrea Bartolini
2023
Abstract
This dataset entry showcases a comprehensive collection obtained from the Tier-0 supercomputer, Marconi A2, hosted at CINECA (https://www.hpc.cineca.it/). The dataset records inlet and outlet temperatures along with power consumption data from 3312 computing nodes, spanning from January 14, 2019, to December 31, 2019. The data is generated through ExaMon, a sophisticated monitoring datacenter infrastructure. The primary objective of this dataset is to support the research and development of HazardNet, an innovative thermal hazard prediction framework tailored specifically for datacenters. HazardNet integrates a comprehensive pipeline of machine-learning models. Researchers and enthusiasts interested in exploring our work further can find the complete set of codes and machine-learning models at our GitHub repository: https://github.com/MSKazemi/HazardNetI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.