Fair and efficient scheduling in data ferrying networks
Why this work is in the frame
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Bibliographic record
Abstract
Data-ferrying disconnection-tolerant networks allow remote rural areas to access the Internet at very low cost, making them viable alternatives to more expensive access technologies such as DSL, CDMA, and dial-up. In such a network, an Internet-based proxy gathers data from the Internet and sends it to a set of edge nodes called "gateways", from which data ferries, such as buses and cars, opportunistically pick up the data using short-range WiFi as they drive past, and deliver it wirelessly to kiosks in remote villages. In this context, we pose the following question: assuming knowledge of ferry schedules, when and to which gateway should the proxy send each data bundle so that the overall delay is minimized and the bandwidth is shared fairly among competing kiosks? We show that a well-known schedule-aware routing scheme proposed in the literature, i.e., EDLQ [11] is far from optimal. Moreover, EDLQ does not provide means to enforce bandwidth allocations. To remedy these problems, we employ a token bucket mechanism to decouple fairness and delay minimization concerns. We also describe a utility-maximizing scheduler based on the classical minimum-cost network flow problem, that finds optimal schedules. Through simulations, we show that our scheme performs at least as well as EDLQ in scenarios that favour EDLQ, yet achieves up to 40% reduction in delay in those that do not.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it