Quantifying the Influence of Intermittent Connectivity on Mobile Edge Computing
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
Mobile edge computing (MEC) is a key technology that enables the deployment of applications (or services) at the proximity of mobile users. However, the performance of mobile edge computing is sensitive to the quality and availability of underlying connection links. It is still unclear to what extent intermittent connectivity affects the performance of mobile edge computing. In this paper, we make the first attempt to quantify the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. Specifically, we propose an analytical framework based on discrete-time Markov chain and derive a closed-form expression of the task processing time under different network conditions. Our model can be further extended to account for the case with group task arrivals. We also conduct extensive simulations to examine the accuracy of our proposed analytical models with both synthetic and real-world user mobility traces. The results show that our model can well capture the influence of intermittent connectivity on MEC. Our model sheds important insights into the impact of intermittent connectivity on task processing in MEC, which we believe should be taken into account when designing future MEC systems.
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 | 0.001 | 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 | 0.001 |
| 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