Online Routing of Stochastically Arriving Bundles in Delay Tolerant Networks
Bibliographic record
Abstract
Future networks will expand current capabilities to service "outpost" regions in performance-challenging environments, in which link connectivity is intermittent, propagation delay may be extremely long, and/or communication conditions may be hostile. In such performance-challenging environments, a network is likely to be partitioned most of the time, and the message delivery mechanisms must have an opportune series of link-by-link transmittals from source to destination. The current Internet suite of protocols is less than adequate for networking in such performance-challenging environments, and new architecture and protocols, based on bundle transport, are being considered. A bundle is a message unit larger than a packet; it comprises a set of data that is sufficiently useful or meaningful to the application without additional data. In this paper, we focus on the online routing decision for a system in which bundle arrival times and bundle sizes are stochastic. We also model the time-varying link rates by stochastic processes. Then, we present an approach to "online" bundle routing based on "offline" traffic engineering. In this approach, we pre-compute nominal traffic flows based on the average available transmission rates of all links and the average rates of offered traffic for all source-destination node pairs. Then, online routing decisions are made on the basis of the nominal flows computed offline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".