Efficient Hashing for Dynamic Per-Flow Network-Interface Selection
Bibliographic record
Abstract
Although most smartphones today have both cellular data and WiFi capacity, network-selection techniques typically only allow for a single data interface to be used at a time. This leads to several problems including the interruption of existing connections when switching interfaces, complete loss of connectivity when the selected network is not functioning correctly, and significantly less bandwidth to the device than is possible if the available wireless capacity is aggregated. While interface bonding is not new, aggregating two such diverse networks is particularly challenging given that different wireless technologies, and even different networks of the same type, offer inconsistent link parameters, presenting dynamically fluctuating bandwidth, latency, and packet loss. In this paper we present a simple, but extremely efficient, hashing technique for multi-interface packet scheduling. We have implemented our system with aggregation proxies running in well-connected data centers and client code running on a Galaxy Nexus running Android 4.1. Our experimental results show that our prototype never loses connectivity even when networks fail as long as at least one network is functioning, does not break connections as interface selection is adjusted, and can increase bandwidth to the smartphone to the available aggregated network capacities. Transmission time is reduced by up to 60% vs. using a single network interface.
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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) | 0.001 | 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".