6G-Empowered Offloading for Realtime Applications in Multi-Access Edge Computing
Bibliographic record
Abstract
Multi-access Edge Computing (MEC) is a promising solution to the resource shortage problem on mobile devices. With MEC, a fraction of the computational tasks on mobile devices could be offloaded to edge servers. Over the past years, a series of machine learning based offloading methods for MEC have been proposed to reduce the completion time of computational tasks. However, most of the existing methods do not work well for realtime applications, which involve tasks with rigorous deadline constraints. In addition, offloading data-intensive tasks via the latest wireless networks, such as LTE and 5G, could lead to unsatisfactory transmission delays. Furthermore, with the state-of-the-art learning-based methods, both the training and inference operation of the learning algorithm are carried out on mobile devices, undesirably leaving less computation resources for computational tasks on mobile devices. In this paper, we propose a 6G-empowered learning-based offloading scheme, MELO, which can be used to make appropriate offloading decisions for realtime tasks. Specifically, the task offloading problem is first formulated as a Markov Decision Process. Thereafter, the problem is solved with a Reinforcement Learning (RL) algorithm, TD3. In addition, 6G is adopted as the communication infrastructure to sufficiently support the data transfer between mobile devices and edge servers. Furthermore, to leave more resources on mobile devices, we devise a novel learning architecture, EALA. With EALA, the training and inference operation of a learning algorithm are decoupled. The training operation is carried out on edge servers while the inference operation is performed on mobile devices. Our experimental results indicate that MELO outperforms the existing offloading methods in terms of task completion time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".