Touchless and always-on cloud analytics as a service
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
Despite modern advances in automation and managed services, many end users of cloud services remain concerned about the lack of visibility into their operational environments. The underlying principles of existing approaches employed to aid users gain visibility into their runtimes do not apply to today's dynamic cloud environment where virtual machines and containers operate as processes of the cloud operating system. We present near field monitoring (NFM), a cloud-native framework for monitoring cloud systems and providing operational analytics services. With NFM, we employ cloud, virtualization, and containerization abstractions to provide extensive visibility into running entities in the cloud, in a touchless manner, i.e., without modifying, instrumenting, or accessing within the end-user context. Operating outside the context of the target systems enables always-on monitoring independent of their health. Using an NFM implementation on OpenStack, we demonstrate the capabilities of NFM, as well as its monitoring accuracy and efficiency. NFM is practical and general, supporting more than 1,000 different system distributions, allowing instantaneous monitoring as soon as a guest system becomes hosted on the cloud, without any setup prerequisites or enforced cooperation.
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 | 0.002 | 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 | 0.000 |
| 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 it