Optimal solution of total routing table size for hierarchical networks
Bibliographic record
Abstract
Hierarchical routing is an effective way to solve the scalability problem in flat networks. The optimal total routing table (RT) size is affected by four parameters: the total number of nodes in a network hierarchy levels, the number of clusters at each level, and cluster size distribution. An optimal solution of total RT size was given in L. Kleinrock et al. (1977). However, the optimal solution was based on a major assumption: all nodes in the network have equal size RTs. It is not clear whether the optimal results stated in L. Kleinrock et al. (1977) still hold without this assumption. We provide the general integer solution of optimal RT sizes without this assumption. In addition, we will show how the total number of nodes, the number of hierarchical levels, and the number of the highest-level clusters affect the total RTsize. Moreover, some important properties of the two-level cluster structure are extensively addressed, namely the impact of the variance of cluster size distribution on intra-cluster update costs and the RTsize.
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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 | 0.000 | 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 | 0.000 | 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 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".