A hop-by-hop delay-constrained routing algorithm with explicit loop avoidance and backup routing information: Research Articles
Bibliographic record
Abstract
QoS Routing is crucial for QoS provisioning in high-speed networks. In general, QoS routing can be classified into two paradigms: source routing and hop-by-hop routing. In source routing, the entire path to the destination node of a communication request is locally computed at the source node based on the global state that it maintains, which does not scale well to large networks. In hop-by-hop routing, a path-selecting process is shared among intermediate nodes between the source node and the destination node, which can largely improve the protocol scalability.In this paper, we present the design of hop-by-hop routing with backup route information such that each intermediate node can recursively update the best known feasible path, if possible, by collectively utilizing the routing information gathered thus far and the information that it locally stores. Such a route is kept as a backup route and its path cost is used as a reference to guide the subsequent routing process to search for a lower-cost constrained path and avoid performance degradation. In this way, the information gathered is maximally utilized for improved performance. We prove the correctness of our presented algorithm and deduce its worst message complexity to be O(mVm2), where mVm is the number of network nodes. Simulation results indicate that, however, the designed algorithm requires much fewer messages on average. Therefore it scales well with respect to the network size. Moreover, simulation results demonstrate that the cost performance of our algorithm is near-optimal. Copyright © 2004 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.003 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".