I am interested in the environmental footprint of cloud computing and how system engineering can reduce it, developing methods that help providers accommodate diverse usage at scale.
I currently focus on sharing mechanisms through orchestration and oversubscription.
Questions about real cloud platform challenges? Interested in collaborating? Contact: pierre.{lastname} {at} etsmtl {dot} ca
No Disk Left Behind: Lifetime-Aware Oversubscription of Thin Provisioned Storage in Cloud IaaS platforms, P. Jacquet, C. Coti, M. Dias de Assunção International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2026), 2026 [Code]
Cloud resource oversubscription is widely used to improve infrastructure utilization, particularly for CPU. In contrast, storage resources remain largely statically provisioned despite significant underutilization: our analysis of multiple OVHcloud platforms shows that virtual machines use on average only 42.9% of their local disk capacity. This paper explores disk oversubscription in thin-provisioned environments, where storage resources are persistent and difficult to predict in the black-box context of IaaS platforms. We introduce Disk Release Prediction (DRP), a dynamic oversubscription method that anticipates disk capacity released by instance churn. Using large-scale traces from OVHcloud, we show that DRP can reduce required disk capacity by up to 2x while maintaining a low rate of corrective migrations, lowering storage-related scope 3 emissions by approximately 44.5% in newer cloud regions.
@inproceedings{scdisk,
title = {No Disk Left Behind: Lifetime-Aware Oversubscription of Thin Provisioned Storage in Cloud IaaS platforms},
author = {Pierre Jacquet and Camille Coti and Marcos Dias de Assunção},
booktitle = {International Conference for High Performance Computing, Networking, Storage and Analysis},
year = {2026},
url = {https://zenodo.org/records/21262313},
}
CINERGY: Deterministic Power Monitoring for Carbon Accounting in the Cloud, P. Jacquet, C. Coti, M. Dias de Assunção, R. Rouvoy IEEE Transactions on Cloud Computing (TCC), 2026 [Paper][Code]
Cloud providers now commonly offer tools to monitor the environmental impact of hosted services, a significant share of which arises from the power consumption of servers. To allocate server-related emissions, these methodologies typically rely on resource-quantity-based attribution and do not account for the effective usage of virtual resources. We demonstrate that resource usage can drastically affect carbon estimations, and propose CINERGY, a framework to standardize how cloud providers integrate the power consumption of virtual resources into their carbon calculators. It defines a deterministic baseline for the power consumption of a provisioned VM (same usage, same estimate) while still accounting for consolidation gains enabled by virtualization. Constructed and evaluated empirically, our approach achieves high precision, with a mean absolute error of 6.6% across various hardware. Using metrics collected from cloud providers, we reveal the impact of instance type on energy efficiency (up to 85% differences for the same VM size) and show that quantity-based accounting can underestimate CO2 emissions by up to a factor of three.
@article{cinergytcc,
title = {CINERGY: Deterministic Power Monitoring for Carbon Accounting in the Cloud},
author = {Pierre Jacquet and Camille Coti and Marcos Dias de Assunção and Romain Rouvoy},
journal = {IEEE Transactions on Cloud Computing},
year = {2026},
pages = {822-833},
doi = {10.1109/TCC.2026.3674370},
url = {https://hal.science/hal-05551692},
}
Larger Cloud Servers, Fewer Hosts? On the Evolution of VM Sizes in IaaS Platforms, P. Jacquet, C. Coti, M. Dias de Assunção IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2026
In contrast to hardware, the evolution of the virtual resources through which most practitioners interact with computing infrastructure remains largely unexplored. We study how Virtual Machine (VM) configurations have evolved across major cloud providers over the past two decades, using historical catalogs and large-scale usage traces. Our analysis shows that VM evolution diverges from underlying hardware trends: although provider catalogs have expanded toward larger configurations, client demand remains concentrated on the smallest options. At the same time, larger options are increasingly selected, and gradual increases in minimal allocations have raised the footprint of active VMs, which doubled between 2019 and 2025. Consequently, the number of instances consolidated per host has remained stable over time. By progressively inflating default VM footprints, cloud offerings weaken incentives for software efficiency and limit the potential gains from consolidating workloads on more capable servers.
@inproceedings{cloudevolutionccgrid26,
title = {Larger Cloud Servers, Fewer Hosts? On the Evolution of VM Sizes in IaaS Platforms},
author = {Pierre Jacquet and Camille Coti and Marcos Dias de Assunção},
booktitle = {IEEE International Symposium on Cluster, Cloud and Internet Computing},
year = {2026},
pages = {287-296},
doi = {10.1109/CCGrid68966.2026.00037},
}
Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings, P. Jacquet, M. Agusti, E. Caron, C. Coti, M. Dias de Assunção, L. Lefèvre, A. Orgerie European Conference on Computer Systems (EuroSys), 2026 [Paper][Code]
As the demand for AI-driven workloads increases, the energy consumption of Graphics Processing Units (GPUs) has come under intense scrutiny, particularly in hyperscale data centers where large numbers of accelerators are centralized and leased to diverse clients. However, GPU power monitoring in cloud environments presents several challenges, as the monitoring capabilities of physical devices may be limited or even absent depending on the product offered. We propose methods to estimate the energy consumption of jobs running on GPU devices in cloud environments, spanning shared and managed offerings (ML-as-a-Service) to less managed products (Infrastructure-as-a-Service). Our models demonstrate the benefits of sharing GPUs for small AI workloads, as well as the current sub-optimal utilization of GPUs in cloud hyperscalers, based on insights from an IaaS GPU cluster.
@inproceedings{eurosys26,
title = {Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings},
author = {Pierre Jacquet and Maxime Agusti and Eddy Caron and Camille Coti and Marcos Dias de Assunção and Laurent Lefèvre and Anne-Cécile Orgerie},
booktitle = {European Conference on Computer Systems},
year = {2026},
pages = {624-640},
doi = {10.1145/3767295.3769333},
url = {https://hal.science/hal-05291033},
}
The Case for Time-Shared Computing Resources, P. Jacquet and Adrien Luxey-Bitri Computing Within Limits (LIMITS), 2025 [Paper]
Although the sector appears virtual, Information and Communication Technologies remain inherently constrained by their materiality and cannot rely on an infinite pool of resources. Contrary to common assumptions, we show that tenants typically do not share computing resources, even in environments commonly perceived as mutualized such as cloud platforms: time-sharing has been progressively phased out for reasons of performance, security, predictability, and the decreasing cost of computing resources. This paper advocates for managing fewer physical resources by improving resource sharing between tenants, a paradigm shift moving beyond traditional time-sharing at the hardware level to a higher abstraction. We review the current state of the art, identify challenges and opportunities, and outline key research directions for Time-Shared Computing.
@inproceedings{sharinglimits,
title = {The Case for Time-Shared Computing Resources},
author = {Pierre Jacquet and Adrien Luxey-Bitri},
booktitle = {Computing Within Limits},
year = {2025},
pages = {1-8},
url = {https://hal.science/hal-05130125},
}
Monitoring GPUs in Cloud Platforms: Challenges & Opportunities ~ Program Keynote, 2nd GreenSys workshop, held with EuroSys'26, Edinburgh, UK, April, 2026
Measuring Energy in Hyperscale Cloud Environments ~ Program Keynote, 4th TDIS workshop, held with EuroSys'26, Edinburgh, UK, April, 2026
Dynamic Overcommitment in OpenStack ~ Program Invited talk, OpenInfra Summit, École polytechnique (X), France, October, 2025
I am a postdoctoral fellow at ÉTS Montréal (Canada), and research scientist at OVHcloud Montréal as part of an ongoing collaboration.
I hold a PhD in Computer Science from the University of Lille (France), realized within Spirals and Stack teams from Inria (2021-2024).
I received my Computer Science engineering degree in 2021 from Paris XII University (France), giving master's degree.
Prior to the PhD, I spent three years working on the Mainframe security ecosystem.
