QoS differentiation in switching-based Web caching
Bibliographic record
Abstract
Differentiated services (DiffServ) [1998] are being adopted for various Internet applications, including Web services. In the Web-caching field, researchers have proposed to realize DiffServ on Web servers, cache servers, and the client. We argue that there are significant advantages of implementing DiffServ on edge routers in a distributed Web caching system. Edge routers can perform request classification, and assign the type of service, hence the per-hop behavior of the classified requests. If the edge router has knowledge of each cache server, then the edge router is able to provide quality of service to different requests by forwarding the requests to the most appropriate cache server. We propose a switching-based differentiated service aching scheme that provides different types of service to three classes of requests, namely streaming class, real-time assured class and best-effort class. A detailed simulation model is described and then used to examine the conditions under which our scheme is able to satisfy the service requirements of the three classes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".