Bounds on end-to-end delay and jitter in input-buffered and internally-buffered IP networks
Bibliographic record
Abstract
Bounds on the end-to-end delay, jitter and service lead/lag for all statically-provisioned multimedia traffic flows routed through any network of input-queued (IQ) switches are presented. A recursive fair stochastic matrix decomposition (RFSMD) algorithm is used to determine near-optimal transmission schedules for each switch, where the jitter and service lead/lag of all flows are simultaneously bounded by K middot IIDT time-slots for small constant K, where IIDT denotes the ideal inter-departure time for each flow. It is established that: (a) the number of buffered cells per flow per switch is near-minimal and bounded by O(K) cells, (b) the end-to-end queueing delay along an H-hop path is near-minimal and bounded by O(KH middot IIDT ) time-slots, (c) the end-to-end jitter and service lead/lag are near-minimal and bounded by O(K middot IIDT ) time-slots (the jitter is not cumulative), and (d) all network-introduced jitter can be provably removed using small playback buffers with O(K) cells. It follows that all statically-provisioned traffic flows, including VOIP, IPTV and Video-on-Demand traffic, can be delivered with essentially-perfect QoS even at 100% loads, thereby achieving the optimal statistical multiplexing gain. The bounds also apply when the crossbar switches use a combination of IQs and crosspoint queues. These theories explain several exhaustive results which have recently been presented in the literature.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".