Mobility-Aware Partial Task Offloading and Resource Allocation Based on Deep Reinforcement Learning for Mobile Edge Computing
Bibliographic record
Abstract
Mobile edge computing (MEC) is widely recognized as one of the key solutions for future networks to enhance network performance. A reasonable offloading strategy can enhance network performance and improve quality of service (QoS). However, most of the existing offloading and resource allocation schemes have been developed for static MEC systems, whereas the randomness of user devices movement in cellular networks may lead to inevitable additional migration overhead, posing challenges to MEC task offloading. In this paper, considering the dynamics of stochastic events, including the randomness of user devices movement and the time-varying channel condition, a novel mobility-aware dynamic task offloading scheme based on deep reinforcement learning (DRL-DTO) is designed to minimize the long-term average overhead by jointly optimizing the local CPU resource allocation and edge server selection as well as the offloading rate decision. First, the virtual energy queue and Lyapunov optimization are introduced to address the long-term energy consumption constraint. The formulated stochastic MINLP problem can be further transformed into deterministic MINLP subproblems for each time slot. Then, the transformed subproblem can be further divided into local CPU resource allocation and offloading strategy optimization problems, in which the optimal edge server can be selected based on the maximum transmission rate. The sub-optimal offloading rate decision can be made by DRL. Finally, simulation results demonstrate that the proposed DRL-DTO scheme can achieve less overhead, have less energy consumption, and gain shorter task completion time in a dynamic edge environment compared with other concerned 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".