Caribou: Fine-Grained Geospatial Shifting of Serverless Applications for Sustainability
Bibliographic record
Abstract
Sustainability in computing is critical as environmental concerns rise. The cloud industry's carbon footprint is significant and rapidly growing. We show that dynamic geospatial shifting of cloud workloads to regions with lower carbon emission energy sources, particularly for more portable cloud workloads such as serverless applications, has a high potential to lower operational carbon emissions. To make the case, we build a comprehensive framework called Caribou that offloads serverless workflows across geo-distributed regions. Caribou requires no change in the application logic, nor on the provider side. It dynamically determines the best deployment plans, automatically (re-) deploys functions to appropriate regions, and redirects traffic to new endpoints. In reducing operational carbon through fine-grained, function-level offloading, Caribou does not undermine standard metrics such as performance and cost. We show how this approach can reduce the carbon footprint by an average of 22.9% to 66.6% across the North American continent. We demonstrate how a detailed specification of location constraints (e.g., to ensure compliance of one stage) can allow emission reductions for workflows (e.g., by offloading other stages). By showcasing the feasibility of carbon-aware geospatial application deployment, Caribou aims to push the boundaries of system techniques available to curtail cloud carbon emissions and provide a framework for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".