# CASPER: Carbon-Aware Scheduling and Provisioning for Distributed Web Services

arXiv: https://arxiv.org/abs/2403.14792 · HTML: https://mlsanity.com/paper/2403.14792/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2024.

## Built on by (most cited first, 8 of 8)

- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand](https://mlsanity.com/paper/2404.15211/) (2024)
- [Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning](https://mlsanity.com/paper/2510.03970/) (2025)
- [Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid](https://mlsanity.com/paper/2511.04776/) (2025)
- [DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling](https://mlsanity.com/paper/2604.15594/) (2026)
- [SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Clouds](https://mlsanity.com/paper/2607.22806/) (2026)
- [AgentDecarbonizer: Carbon-Aware Execution for AI Agents](https://mlsanity.com/paper/2608.20566/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

There has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the possibility to adapt the location, time, and intensity of processing to align with the availability of renewable and low-carbon energy. An example is a web application hosted across multiple cloud regions, each with varying carbon intensity based on their local electricity mix. Distributed load-balancing enables the exploitation of low-carbon energy through load migration across regions, reducing web applications carbon footprint. In this paper, we present CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO). We formulate CASPER as an multi-objective optimization problem that considers both the variable carbon intensity and latency constraints of the network. Our evaluation reveals the significant potential of CASPER in achieving substantial reductions in carbon emissions. Compared to baseline methods, CASPER demonstrates improvements of up to 70% with no latency performance degradation.