Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Recently, the technological development in edge computing and content caching can provide high-quality services for users in the wireless communication networks. As a promising technology, multi-access edge computing (MEC) can offload tasks to the nearby edge servers, which alleviates the pressure of users. However, various services and dynamic wireless channel conditions make effective resource allocation challenging. In addition, network slicing can create a logical virtual network and allocate resources flexibly among multiple tenants. In this paper, we construct an integrated architecture of communication, computing and caching to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited resources, this paper adopts an improved deep reinforcement learning (DRL) method, which fully jointly considers the diversity of user request services and the dynamic wireless channel conditions to obtain the mobile virtual network operator (MVNO) maximal profit function. Considering the slow convergence speed of the DRL algorithm, this paper combines DRL and ensemble learning. The simulation result shows that the resource allocation scheme inspired by DRL is significantly better than the other compared strategies. The output of the result of DRL algorithm combined with ensemble learning is faster and more cost-effective.
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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 | 0.001 | 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 | 0.001 |
| 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 it