Soft QoS provisioning using the token bank fair queuing scheduling algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Future-generation wireless packet networks will support multimedia applications with diverse QoS requirements. Much of the research on scheduling algorithms has been focused on hard QoS provisioning of integrated services. Although these algorithms give hard delay bounds, their stringent requirements sacrifice the potential statistical multiplexing performance and flexibility of the packet-switched network. Furthermore, the complexities of the algorithms often make them impractical for wireless networks. There is a need to develop a packet scheduling scheme for wireless packet-switched networks that provides soft QoS guarantees for heterogeneous traffic, and is also simple to implement and manage. This article proposes token bank fair queuing (TBFQ), a soft scheduling algorithm that possesses these qualities. This algorithm is work-conserving and has a complexity of O(1). We focus on packet scheduling on a reservation-based TDMA/TDD wireless channel to service integrated real-time traffic. The TBFQ scheduling mechanism integrates the policing and servicing functions, and keeps track of the usage of each connection. We address the impact of TBFQ on mean packet delay, violation probability, and bandwidth utilization. We also demonstrate that due to its soft provisioning capabilities, the TBFQ performs rather well even when traffic conditions deviate from the established contracts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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