Safety-Critical Offloading with Constrained Reinforcement Learning for Multi-access Edge Computing
Bibliographic record
Abstract
The proliferation of computation-intensive applications, such as autonomous driving, has urged mobile devices to alleviate their local computation pressure using external computing resources. As a promising solution, Multi-access Edge Computing tackles this problem by offloading computational tasks from mobile devices to edge servers. However, existing offloading schemes suffer from two fundamental limitations. First, they lack built-in measures to prevent deadline misses. For safety-critical applications, including autonomous driving, a deadline miss could result in catastrophic consequences. Second, existing schemes typically update offloading policies periodically. Namely, a policy based on the current system state is generated for a time window consisting of multiple time slots. Since system states could change from one time slot to the next one, the generated policy might not work well during the entire window. In this article, we propose a novel offloading scheme for safety-critical applications, Constrained Reinforcement Learning-based Offloading (CRLO). With CRLO, a safety layer is added to the learning-based policy generator, which effectively eliminates deadline misses. Furthermore, a long-sequence forecasting model, Informer, is utilized to predict temporally dependent system states, which helps to generate appropriate offloading policies. Our experimental results indicate that CRLO outperforms existing schemes in terms of deadline satisfaction and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".