Joint Server Selection, Cooperative Offloading and Handover in Multi-access Edge Computing Wireless Network: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Multi-access edge computing (MEC) is the key enabling technology that supports compute-intensive applications in 5G networks. By deploying powerful servers at the edge of wireless networks, MEC can extend the computational capacity of the mobile devices by migrating compute-intensive tasks to the MEC servers. In this paper, we consider a multi-user MEC wireless network in which multiple mobile devices can associate and perform computation offloading via wireless channels to MEC servers attached to the base stations (BSs). The decision whether the computation task is executed locally at the user device or to be offloaded for MEC server execution should be adaptive to the time-varying network dynamics. Taking into account the dynamic of the environment, we propose a deep reinforcement learning (DRL) based approach to solve the formulated nonconvex problem of minimizing computation cost in terms of total delay. However, real-world networks tend to have a large number of users and MEC servers involving large numbers of different actions (continuous and discrete), where evaluating the combination of every possible action becomes impractical. Therefore, conventional DRL methods may be difficult or even impossible to directly apply to the proposed model. Based on the recursive decomposition of the action space available to each state, we propose a DRL-based algorithm for joint server selection, cooperative offloading, and handover in a multi-access edge wireless network. Numerical results show that the proposed DRL based algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".