M3SA: Exploring Datacenter Performance and Climate-Impact with Multi- and Meta-Model Simulation and Analysis
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918420507787264 |
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| author | Nicolae, Radu Niewenhuis, Dante Talluri, Sacheendra Iosup, Alexandru |
| author_facet | Nicolae, Radu Niewenhuis, Dante Talluri, Sacheendra Iosup, Alexandru |
| contents | Datacenters are vital to our digital society, but consume a considerable fraction of global electricity and demand is projected to increase. To improve their sustainability and performance, we envision that simulators will become primary decision-making tools. However, and unlike other fields focusing on key societal infrastructure such as waterworks and mass transit, datacenter simulators do not yet combine multiple independent models into their operation and thus suffer from issues associated with singular models, such as specialization, and lack of adaptability to operational phenomena. To address this challenge, we propose M3SA, a datacenter simulation and analysis framework that uses discrete-event simulation to predict, for each model, the impact on climate and performance under various realistic datacenter conditions, and then combines these predictions. We design an architecture for simulating multiple concurrent models (Multi-Model), a technique for integrating the results of multiple models into a Meta-Model, and a procedure for quantifying Meta-Model accuracy. Through experiments with an M3SA prototype, we show that (i) M3SA can reproduce and enhance peer-reviewed experiments; (ii) M3SA can predict operational phenomena (e.g., failures) of datacenters, running fundamentally different workload traces; (iii) M3SA enables various types of what-if and how-to analysis, such as how to configure CO2-aware migration over yearly energy-production patterns. M3SA has been integrated into the open-source simulator OpenDC and is available at: https://github.com/atlarge-research/opendc-m3sa. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29778 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | M3SA: Exploring Datacenter Performance and Climate-Impact with Multi- and Meta-Model Simulation and Analysis Nicolae, Radu Niewenhuis, Dante Talluri, Sacheendra Iosup, Alexandru Distributed, Parallel, and Cluster Computing Datacenters are vital to our digital society, but consume a considerable fraction of global electricity and demand is projected to increase. To improve their sustainability and performance, we envision that simulators will become primary decision-making tools. However, and unlike other fields focusing on key societal infrastructure such as waterworks and mass transit, datacenter simulators do not yet combine multiple independent models into their operation and thus suffer from issues associated with singular models, such as specialization, and lack of adaptability to operational phenomena. To address this challenge, we propose M3SA, a datacenter simulation and analysis framework that uses discrete-event simulation to predict, for each model, the impact on climate and performance under various realistic datacenter conditions, and then combines these predictions. We design an architecture for simulating multiple concurrent models (Multi-Model), a technique for integrating the results of multiple models into a Meta-Model, and a procedure for quantifying Meta-Model accuracy. Through experiments with an M3SA prototype, we show that (i) M3SA can reproduce and enhance peer-reviewed experiments; (ii) M3SA can predict operational phenomena (e.g., failures) of datacenters, running fundamentally different workload traces; (iii) M3SA enables various types of what-if and how-to analysis, such as how to configure CO2-aware migration over yearly energy-production patterns. M3SA has been integrated into the open-source simulator OpenDC and is available at: https://github.com/atlarge-research/opendc-m3sa. |
| title | M3SA: Exploring Datacenter Performance and Climate-Impact with Multi- and Meta-Model Simulation and Analysis |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2603.29778 |