Kubernetes or OpenShift? Which Technology Best Suits Eclipse Hono IoT Deployments
Bibliographic record
Abstract
New verticals within the Internet of Things paradigm, i.e., smart cities, industrie 4.0, etc., require specific platform(s) to allow different components to communicate. The value of the IoT systems often correlates directly with the ability of those platforms to connect different devices efficiently and integrate them into higher-level solutions. Eclipse Hono allows the provisioning of remote service interfaces for connecting devices to a back-end and interacts with them uniformly regardless of their types and communication protocols. Currently, there is a variety of possibilities for using Hono in production; it can be deployed on Kubernetes, OpenShift or Docker Swarms. However, these deployments decisions have important performance implications that the developers are not often aware of. In this paper, we step up loads in Kubernetes and OpenShift to clear out the performance costs of their deployment scenarios, with the aim to provide the practitioners with guidelines to help understand the performance implications of their design and deployment decisions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".