Bridging the Sustainability Gap in Serverless through Observability and Carbon-Aware Pricing
Bibliographic record
Abstract
Serverless computing has become a mainstream cloud computing paradigm due to its high scalability, ease of server management, and cost-effectiveness. With cloud data centers' carbon footprint rising sharply, understanding and minimizing the carbon impact of serverless functions becomes crucial. The unique characteristics of serverless functions, such as event-driven invocation, pay-as-you-go billing model, short execution duration, ephemeral runtime, and opaque underlying infrastructure, pose challenges in effective carbon metering. In this paper, we argue that the current carbon estimation methodologies should be expanded for more accurate carbon accounting in serverless settings, and propose a usage and allocation-based carbon model that aligns with the context of serverless computing. We also articulate how current serverless systems and billing models do not make it financially attractive to prioritize sustainability for a broad class of users and developers. To solve this, we propose a new carbon-aware pricing model and evaluate its ability to incentivize sustainable practices for developers through better alignment of billing and carbon efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".