EC6: Enhancing Energy Efficiency in Kubernetes Through Dynamic Extension of CPU Deep Idle States (C6)
Bibliographic record
Abstract
Although the energy footprint of cloud infrastructures is becoming a critical concern, mainstream orchestration platforms such as Kubernetes (K8s) continue to prioritize performance over power efficiency. By default, K8s employs static CPU pinning for latency-sensitive services to ensure predictable performance and prevent resource contention. However, during service idle periods, residual background activities-such as runtime threads and maintenance tasks-occupy the assigned cores, preventing them from entering deep idle states (e.g., C6) and leading to unnecessary power consumption. This paper introduces EC6, a lightweight, idle-aware scheduling policy for K8s that consolidates idle services onto a reserved single CPU core, allowing other cores to transition into deep C-states during service inactivity. Upon resumption of service activity, EC6 transparently restores services to their original CPU cores with minimal latency, ensuring resource allocation constraints (i.e., , number of CPU cores) are maintained. Experimental evaluations show that EC6 increases per-core C6 residency by up to 30% and reduces idle power consumption by up to 13.6% compared to K8s' default container scheduling policy, without compromising performance. This demonstrates its effectiveness in improving energy proportionality in cloud-native environments while preserving service quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".