3GC: A Deadline-Aware and Energy-Efficient Resource Allocation Scheme for Serverless Edge Computing
Bibliographic record
Abstract
To align with sustainability goals, such as Net Zero Emissions, serverless providers must incorporate energy considerations into their resource management models. In edge computing environments, where resources are scarce and latency requirements are stringent, serverless frameworks like OpenFaaS and OpenWhisk commonly rely on Kubernetes for function allocation on edge nodes. However, these frameworks often overlook energy consumption as a critical decision factor, which leads to increased energy costs and higher operational expenses (OPEX). To address this gap, we introduce Go-Green-Go-Cheap (3GC) approach-a deadline-aware and cost-effective resource allocation strategy tailored for serverless service providers to minimize energy and execution costs. 3GC enables real-time resource allocation and leverages the per-core Dynamic Voltage and Frequency Scaling (DVFS) feature to finely tune the balance between execution time and energy consumption. Our evaluation demonstrates that 3GC surpasses existing allocation techniques, achieving cost savings of 39.35% up to 69.43%, while consistently meeting function latency requirements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".