Estimating CO2 emissions of distributed applications and platforms with SimGrid_Batsim
Resumo
This work presents a carbon footprint plugin designed to extend the capabilities of the Batsim simulator by allowing the calculation of CO2 emissions during simulation runs. The goal is to assess the environmental impact associated with task and resource management strategies in simulated environments. The plugin is developed within SimGrid—the underlying simulation framework of Batsim—and computes carbon emissions based on the simulated platform’s energy consumption and carbon intensity factor of the simulated machines. Once implemented, it is integrated into Batsim, ensuring compatibility with existing simulation workflows and enabling researchers to assess the carbon efficiency of their scheduling strategies.
Referências
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Calheiros, R. N., Ranjan, R., Beloglazov, A., Rose, C. A. F. D., and Buyya, R. (2011). Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50.
Casanova, H., Giersch, A., Legrand, A., Quinson, M., and Suter, F. (2014). Versatile, scalable, and accurate simulation of distributed applications and platforms. Journal of Parallel and Distributed Computing, 74(10):2899–2917.
Chasapis, D., Moretó, M., Schulz, M., Rountree, B., Valero, M., and Casas, M. (2019). Power efficient job scheduling by predicting the impact of processor manufacturing variability. In Proceedings of the ACM International Conference on Supercomputing, pages 296–307.
Dutot, P.-F., Mercier, M., Poquet, M., and Richard, O. (2016). Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator. In 20th Workshop on Job Scheduling Strategies for Parallel Processing.
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Kliazovich, D., Bouvry, P., and Khan, S. U. (2012). Greencloud: A packet-level simulator of energy-aware cloud computing data centers. The Journal of Supercomputing, 62(3):1263–1283.
Masanet, E., Shehabi, A., Lei, N., Smith, S., and Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481):984–986.
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Poquet, M. (2017). Simulation approach for resource management. Data structures and algorithms, Université Grenoble Alpes.
Vasconcelos, M., Cordeiro, D., da Costa, G., Dufossé, F., Nicod, J.-M., and Rehn-Sonigo, V. (2023). Optimal sizing of a globally distributed low carbon cloud federation. In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pages 203–215.
Zhang, X., Zhong, J., Zhang, X., Zhang, D., Miao, C., Wang, D., and Guo, L. (2025). China can achieve carbon neutrality in line with the Paris agreement’s 2°C target: Navigating global emissions scenarios, warming levels, and extreme event projections. Engineering, 44:207–214.
Calheiros, R. N., Ranjan, R., Beloglazov, A., Rose, C. A. F. D., and Buyya, R. (2011). Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50.
Casanova, H., Giersch, A., Legrand, A., Quinson, M., and Suter, F. (2014). Versatile, scalable, and accurate simulation of distributed applications and platforms. Journal of Parallel and Distributed Computing, 74(10):2899–2917.
Chasapis, D., Moretó, M., Schulz, M., Rountree, B., Valero, M., and Casas, M. (2019). Power efficient job scheduling by predicting the impact of processor manufacturing variability. In Proceedings of the ACM International Conference on Supercomputing, pages 296–307.
Dutot, P.-F., Mercier, M., Poquet, M., and Richard, O. (2016). Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator. In 20th Workshop on Job Scheduling Strategies for Parallel Processing.
Google (2025). Google environmental report 2025.
International Energy Agency (IEA) (2024). Analysis and forecast to 2026. Technical report, IEA, France.
Kliazovich, D., Bouvry, P., and Khan, S. U. (2012). Greencloud: A packet-level simulator of energy-aware cloud computing data centers. The Journal of Supercomputing, 62(3):1263–1283.
Masanet, E., Shehabi, A., Lei, N., Smith, S., and Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481):984–986.
Microsoft (2025). Microsoft sustainability report.
Orgerie, A.-C. (2020). From Understanding to Greening the Energy Consumption of Distributed Systems. Habilitation à diriger des recherches (HDR), Ecole Normale Supérieure de Rennes.
Poquet, M. (2017). Simulation approach for resource management. Data structures and algorithms, Université Grenoble Alpes.
Vasconcelos, M., Cordeiro, D., da Costa, G., Dufossé, F., Nicod, J.-M., and Rehn-Sonigo, V. (2023). Optimal sizing of a globally distributed low carbon cloud federation. In 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pages 203–215.
Zhang, X., Zhong, J., Zhang, X., Zhang, D., Miao, C., Wang, D., and Guo, L. (2025). China can achieve carbon neutrality in line with the Paris agreement’s 2°C target: Navigating global emissions scenarios, warming levels, and extreme event projections. Engineering, 44:207–214.
Publicado
28/10/2025
Como Citar
SARAIVA, Gabriella; VASCONCELOS, Miguel; BRUSCHI, Sarita Mazzini; CARASTAN-SANTOS, Danilo; CORDEIRO, Daniel.
Estimating CO2 emissions of distributed applications and platforms with SimGrid_Batsim. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 26. , 2025, Bonito/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 109-120.
DOI: https://doi.org/10.5753/sscad.2025.15853.
