Bandwidth and Task Flow Control in Stochastic Relay-Assisted MEC
Bibliographic record
Abstract
This letter studies stochastic Multi-hop Mobile Edge Computing (MMEC), where a Relay Node (RN) receives tasks from the users and randomly decides to assign them to its own computing server or to offload them to one of the multiple Higher-level computing Nodes (HNs). We take into account the waiting times in the computation and transmission queues and present the system Average Response Time (ART) in terms of offloading probabilities and links’ bandwidths. Based on that, we aim at minimizing the system ART by joint optimization of the task flow distribution among the RN/HNs and the bandwidth allocation among the links. We analyze the formulated problem and prove it is multi-convex. Moreover, we derive the closed-form expressions for the links’ bandwidths when the offloading probabilities are fixed, and based on the presented analysis and insights, we propose effective solution methods. Numerical results reveal the promising performance of the proposed methods in efficient use of the resources and reduction of the ART.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".