Bounds on end-to-end delay and jitter in input-buffered and internally-buffered IP networks
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it