Performance Modeling, Analysis, and Optimization of Delayed Mobile Data Offloading for Mobile Users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Operators have recently resorted to Wi-Fi offloading to deal with increasing data demand and induced congestion. Researchers have further suggested the use of delayed offloading: if no Wi-Fi connection is available, (some) traffic can be delayed up to a given deadline or until WiFi becomes available. Nevertheless, there is no clear consensus as to the benefits of delayed offloading, with a couple of recent experimental studies largely diverging in their conclusions, nor is it clear how these benefits depend on network characteristics (e.g., Wi-Fi availability), user traffic load, and so on. In this paper, we propose a queueing analytic model for delayed offloading, and derive the mean delay, offloading efficiency, and other metrics of interest, as a function of the user's patience, and key network parameters for two different service disciplines (First Come First Served and Processor Sharing). We validate the accuracy of our results using a range of realistic scenarios and real data traces. Finally, we use these expressions to show how the user could optimally choose deadlines by solving the variations of a constrained optimization problem, in order to maximize her own benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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 it