Hub routing for the robust network design problem
Bibliographic record
Abstract
Robust network design (RND) applies the concept of robustness from optimization with uncertainty to the area of network design. Primary motivations stem from applications in telecommunication networks. The main presupposition is that demands across the networks are variable or unpredictable. They originate from a predefined demand set, called a demand universe. Moreover, practical impediments of network design enforce the routing of the demands to be oblivious, or fixed in advance, and to not depend on a particular instantiation from the demand universe. Additional restrictions, referred to as a routing model, are often enforced on the routing's structure. Shortest paths (SP) and hub (HUB) routing models have received particular attention, both on the theoretical and practical level. In this work, we introduce a new routing model, called the hierarchical hub routing model (HH), as a generalization to HUB. We study the theoretical properties of RND restricted to HH (RNDHH). Namely, we show its APX-hardness and provide a O(log n)-approximation algorithm. We then show how RNDHH is tractable when the problem is constrained to a particular demand universe based on demands routable on a tree. We also compare the costs of optimal solutions to RND using HH and other important oblivious routing models. Finally, we leverage HH in a practical study of a new demand universe called the capped hose model, which is a blend of the hose and the pipe model, two widely used demand universes. We use the capped hose model to shed light on which demand universes favour more a SP-like as opposed to a HH-like routing. To do so, we develop a heuristic algorithm for RNDHH, and benchmark our approach against SP using representative carrier networks and a variety of capped hose demands, parametrized by their similitude to a hose or pipe model. This study reveals conditions under which multi-hub routings, that is HH, gives improvements over single-hub and shortest path routings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".