Dataset : Optimal sizing of a globally distributed low carbon cloud federation

General metadata

Identifiers :

local : FR-13002091000019-2023-02-03 external : doi:10.25666/DATAUBFC-2023-02-03

Description :

The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model.

Disciplines :

computer science, hardware & architecture (engineering science), operations research & management science (engineering science)

Keywords :

carbon footprint, data centres, energy storage, follow-the-sun, green computing, linear program, renewable energy, scheduling

Dates :

Data acquisition : from Apr 2022 ongoing

Data provision : 1 Feb 2023

Metadata record : Creation : 3 Feb 2023 Update : 16 Mar 2023

Update periodicity : as needed

Language : English (eng)

Audience : Research, Policy maker

Access to data

Rights

Quotation

Miguel Vasconcelos, Daniel Cordeiro, Georges Da Costa, Fanny Dufossé, Jean-Marc Nicod, Veronika Rehn-Sonigo (2023): Optimal sizing of a globally distributed low carbon cloud federation. GitLab. doi:10.25666/DATAUBFC-2023-02-03

Administrative metadata

Data creators : Miguel Vasconcelos ^[1] ^[2], Daniel Cordeiro ^[2], Georges Da Costa ^[3], Fanny Dufossé ^[1], Jean-Marc Nicod ^[4], Veronika Rehn-Sonigo ^[5]

[1] : Laboratoire d'Informatique de Grenoble

[2] : University of São Paulo

[3] : Institut de recherche en informatique de Toulouse

[4] : Franche-Comté Electronique Mécanique Thermique et Optique - Sciences et Technologies (UMR 6174) (École Nationale Supérieure de Mécanique et des Microtechniques)

[5] : Franche-Comté Electronique Mécanique Thermique et Optique - Sciences et Technologies (UMR 6174) (Université de Franche-Comté)

Publisher : GitLab

Science contact : Jean-Marc Nicod

Computing contact : Miguel Vasconcelos

Access : available

Technical metadata

Formats : application/json, application/x-sh, text/csv, text/plain, text/x-python

Data acquisition methods :

Simulation or computational data :
Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration.

Datatype : Dataset

Publications

Miguel Vasconcelos, Daniel Cordeiro, Georges Da Costa, Fanny Dufossé, Jean-Marc Nicod and Veronika Rehn-Sonigo. Optimal sizing of a globally distributed low carbon cloud federation. in Cluster, Cloud and Internet Computing'23, Bangalore, India, May 2023. (to appear)