Design and Implementation of K42's Dynamic Clustered Object Switching Mechanism
Bibliographic record
Abstract
Design and Implementation of K42's Dynamic Clustered Object Switching Mechanism Kevin Hui Master of Science Graduate Department of Computer Science University of Toronto 2000 Recent research efforts have investigated customizable operating systems, where the implementation of operating system services can be chosen to meet an application's performance or functionality requirements. This dissertation investigates the potential benefits of allowing the customization to be changed, on-the-fly, while the service is in use. By using a prototype implementation of the dynamic object switching layer in the K42 operating system, we explore the costs and benefits associated with dynamic customization. As an example, we showed how K42 can switch a (per-file) page cache from a centralized implementation to one distributed across the processors of a multiprocessor in order to adapt to changing access patterns. The ability to customize on-the-fly allows the implementation of a service to match the instantaneous demands on the service, avoiding the need to comprise a complex, catch-all implementation. It also facilitates live-swapping of system components in mission-critical systems where downtime is undesirable.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".