On Maximizing Reliability of Network Topology Design Using a Practical Dynamic Programming Approach
Bibliographic record
Abstract
This paper addresses an NP-complete problem of designing a network topology (NT) with the maximum 2-terminal reliability (R) subject to a cost constraint (C). More specifically, given the locations of the various computer centers (nodes), their connecting links, each link’s reliability and cost, and the maximum budget cost to install the links, the NT design problem, called NTD-RC, aims to find an NT that has the maximum reliability with cost within the budget. Since cost is a major issue in NT design, NTD-RC is applicable for critical applications requiring maximized reliability. This paper formulates a dynamic programming (DP) scheme to help solve NTD-RC. A DP approach, called Algo-DP, finds the set of links to be deleted from the original network to obtain an optimal NT. The paper proposes five-link ordering criteria to improve the performance of Algo-DP. Simulation results on different benchmark networks of various sizes are used to compare Algo-DP with existing techniques in the literature and show the merits of using the sorting methods, and the effectiveness of our algorithm. We found that Algo-DP generates NT with the same or better 2-terminal reliability measure (with up to 4.3% improvement) on 92% of the network topologies. Results indicate Algo-DP demonstrated better performance than other existing algorithm. Furthermore, Algo-DP shows that it is computationally more efficient compared to the recent existing approach.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".