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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".