Kubernetes or OpenShift? Which Technology Best Suits Eclipse Hono IoT Deployments
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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