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 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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".