Bridging the Sustainability Gap in Serverless through Observability and Carbon-Aware Pricing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 it