Usage based service differentiation for end-to-end quality of service management
Bibliographic record
Abstract
It is often difficult to ensure that every application in a computing environment receives the level of quality of service required by their users. In such cases, the demand for computing resources to do so simply exceeds the limited supply that is available. To ensure that at least some user applications meet quality of service requirements, service differentiation is one approach growing in popularity. In this approach, preferential service is given to selected applications, while others deemed less important suffer in comparison. Most work in this area bases service differentiation decisions on static information about the applications, such as the name and type of application, the owner of the application, execution time, and the host on which the application was executed. In this paper, we discuss a new approach to service differentiation that takes into consideration dynamic application usage information in service differentiation decisions. In doing so, we can make better or fairer service differentiation decisions that allow more users to enjoy higher levels of quality of service. This is accomplished by ensuring preferential service is given to applications whose users can actually benefit from the improved service, as opposed to those applications that are essentially ignored or in a state that renders them unusable.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".