Performance Evaluation of Kubernetes Distributions (K8s, K3s, KubeEdge) in an Adaptive and Federated Cloud Infrastructure for Disadvantaged Tactical Networks
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
The tactical edge domain, primarily consisting of dismounted soldiers and vehicles on the move, are typically interconnected via wireless tactical networks that are limited in terms of bandwidth, reachability, reliability, and latency. Hence, nodes in the tactical network cannot simply rely on assured access to enterprise cloud computing. Instead, they must explore other alternative models to leverage resources that are in situ, by means of a federated cloud architecture that spans the three tiers of dismounted soldiers, vehicles on the move, and operations centers. The NATO IST-168 RTG has been exploring approaches to best exploit available resources in such a federated architecture while living within the constraints of the tactical networks. The first approach has been to evaluate Kubernetes technologies to see if they are able to be deployed over tactical networks and provide the capabilities to dynamically distribute data and computing tasks over a federated cloud infrastructure composed of multiple partner nation networks. This paper provides initial performance results for various Kubernetes distributions (K8s, K3s, KubeEdge) in federated and adaptive tactical networks, leading to recommendations for further development and experimentation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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