Datacenters are at the heart of the AI, Industry 4.0 and cloud revolution. A datacenter contains a large number of computing nodes hosted in a large temperature-controlled room. Due to the increasing total power and power density of computing nodes, the overall datacenter compute capacity is often capped by peak power consumption and temperature bottlenecks. To preserve the homogeneous performance assumption between all the nodes, complex cooling solution are required, but they might not be sufficient. In this work, we analysed and characterised the thermal properties of a Tier0 datacenter deploying advanced hybrid cooling technologies: specifically, we studied the spatial and temporal heterogeneity during production and cooling emergency hazards. This paper gives first quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial thermal heterogeneity which could be exploited by thermal-aware job scheduling and datacenter-room run-time workload adaptation and distribution.

Thermal Characterization of a Tier0 Datacenter Room in Normal and Thermal Emergency Conditions

Seyedkazemi Ardebili M.
;
Benini L.
;
Bartolini A.
2021

Abstract

Datacenters are at the heart of the AI, Industry 4.0 and cloud revolution. A datacenter contains a large number of computing nodes hosted in a large temperature-controlled room. Due to the increasing total power and power density of computing nodes, the overall datacenter compute capacity is often capped by peak power consumption and temperature bottlenecks. To preserve the homogeneous performance assumption between all the nodes, complex cooling solution are required, but they might not be sufficient. In this work, we analysed and characterised the thermal properties of a Tier0 datacenter deploying advanced hybrid cooling technologies: specifically, we studied the spatial and temporal heterogeneity during production and cooling emergency hazards. This paper gives first quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial thermal heterogeneity which could be exploited by thermal-aware job scheduling and datacenter-room run-time workload adaptation and distribution.
2021
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1
16
Seyedkazemi Ardebili M.; Cavazzoni C.; Benini L.; Bartolini A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/876296
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