Reinforcement Learning Based Offloading for Realtime Applications in Mobile Edge Computing
Bibliographic record
Abstract
Energy consumption is one of the most important issues for mobile devices such as smartphones and laptops. For mobile devices that execute multiple computation-intensive or delay-sensitive applications simultaneously, Mobile Edge Computing (MEC) based offloading provides a promising solution to the energy problem. However, blindly offloading all tasks to MEC servers is not the best choice because transferring a simple task to a MEC server via wireless networks might consume more energy than processing the task locally. In addition, Dynamic Voltage and Frequency Scaling (DVFS) could be utilized to reduce the energy consumption associated with locally processed tasks by appropriately lowering CPU frequency. In this paper, we propose a realtime reinforcement learning based offloading scheme, RRLO, which is based on both MEC-based offloading and DVFS-based energy consumption reduction. Technically, RRLO jointly learns the optimal offloading policy and DVFS-based scheduling method. Depending on the workload and network condition, RRLO not only determines whether a task should be offloaded to a MEC server, but also selects the best DVFS method used to schedule local tasks. Our simulation results indicate that RRLO outperforms the existing MEC-based offloading schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".