Reinforcement-Learning-Based Task Offloading in Edge Computing Systems with Firm Deadlines
Bibliographic record
Abstract
Task offloading in mobile edge computing systems is subject to various random factors including the connection to external servers, new task requests from users, and the availability of local processing services. However, statistical information is often not available in practical scenarios. To tackle the issue, we adopt a Q-learning-based approach that learns the optimal task offloading policy through observations of random events. Traditional Q-learning methods may face challenges such as long training times and high memory usage due to the large state and action space. To overcome this problem, we propose a novel method that leverages the concept of adjacent state sequence. In this type of sequence, we can infer the optimal offloading decision of a system state from other states. This method aims to improve the convergence speed and memory efficiency of the learning model by reducing the number of parameters that need to be learned and stored. Those eliminated parameters instead can be computed via a derived linear expression. We conduct experiments to demonstrate the enhancement of our proposed method compared to the traditional$\mathbf{Q}-$learning in the studied problem.
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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 | 0.004 |
| 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".