Memory requirements for future Internet routers with essentially-perfect QoS guarantees
Bibliographic record
Abstract
The theory of a future Internet network which achieves essentially-perfect QoS guarantees for all QoS-enabled traffic flows for all loads ≤ 100% of capacity has recently been established. A scheduling algorithm with a bounded normalized service lead/lag (NSLL) is used to schedule traffic flows within the routers. An 'Application-Specific Token-Buffer Traffic Shaper' is used at the traffic sources, to achieve a bounded NSLL on incoming bursty traffic flows. An 'Application-Specific Playback Queue' is used to perfectly regenerate the original busty traffic flows at every destination. Under these conditions, it has been established that every QoS-enabled flow: (i) is delivered with essentially-perfect end-to-end QoS guarantees, and (ii) buffers O(K) cells/packets per router, where K is the bound on the NSLL. In this paper, we reduce the router buffering requirements significantly, so that each router buffers ≤ one cell/packet per QoS-enabled traffic flow, a reduction of up to 1K-10K over existing technologies. The proposed technology can be incorporated into new routers with negligible hardware cost, and is compatible with existing IntServ, DiffServ, MPLS and RSVP-TE protocols.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".