HTR: A Joint Approach for Task Offloading and Resource Allocation in Mobile Edge Computing
Bibliographic record
Abstract
With the proliferation of wireless networks, such as WiFi and LTE/5G, Mobile Edge Computing (MEC), is expected to be a promising solution to the resource constraint problem in mobile devices. Technically, MEC is composed of two types of devices: resource-hungry end devices and resource-rich base stations equipped with edge servers. Despite the popularity of MEC, efficient task offloading and resource allocation have been two challenging problems to be tackled. In this paper, we propose an innovative scheme, HTR, that jointly solves the task offloading and resource allocation problem in MEC. Specifically, the problem of task offloading and resource allocation is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem. To reduce the computation complexity of the solution to the MINLP problem, HTR decouples the MINLP problem into two sub-problems: one of them solves the resource allocation problem while the other tackles the task offloading issue. With this carefully-designed approach, both the task offloading and resource allocation problem could be solved with light computation complexity. Our experiment results indicate the HTR outperforms the existing task offloading/resource allocation schemes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".