Dynamic Admission Control and Resource Allocation for Mobile Edge Computing Enabled Small Cell Network
Bibliographic record
Abstract
Mobile edge computing (MEC) has recently risen as a promising paradigm to meet the increasing resource requirements of the terminal devices. Meanwhile, small cell network (SCN) with MEC has been emerging to handle the exponentially increasing data traffic and improve the network coverage, and is recognized as one key component of the next generation wireless networks. However, with the growing number of terminal devices requiring computation offloading to the edge servers, the network would be heavily congested and thus the performance would be degraded and unbalanced among multiple devices. In this paper, we propose the joint admission control and computation resource allocation in the MEC enabled SCN, and formulate it as a stochastic optimization problem. The goal is to maximize the system utility combining the throughput and fairness while bounding the queue. We decouple the original problem into three independent subproblems, which can be solved in a distributed manner without requiring the system statistical information. An admission control and computation resource allocation (ACCRA) algorithm is designed to obtain the optimal solutions of the subproblems. Theoretical analysis proves that the ACCRA algorithm can achieve the close-to-optimal system utility and reach the arbitrary tradeoff between the utility and the queue length. Experiments are conducted to validate the derived analytical results and evaluate the performance of the ACCRA algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".