An End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control
Bibliographic record
Abstract
The infrastructure to vehicle (I2V) communication boosts a large number of prevailing vehicular services, which can provide vehicles with external information, storage, and computing power located at both mobile edge server (MES) and remote cloud. However, vehicle distribution is imbalanced due to the spatial inhomogeneity and temporal dynamics. As a consequence, the communication load for MES is imbalanced and vehicles may suffer from poor I2V communications where the MES is overloaded. In this paper, we propose a novel proactively load balancing approach that enables efficient cooperation among MESs, which is referred to as end-to-end load balancer (E2LB). E2LB schedules the cached data among MESs based on the predicted road traffic situation. First, a convolutional neural network (CNN) is applied to efficiently learn the spatio-temporal correlation in order to predict the road traffic situation. Then, we formulate the load balancing problem as a nonlinear programming (NLP) problem and a novel framework based on CNN is adopted to approximate the NLP optimization. Finally, we connect the above neural networks into an end-to-end neural network to jointly optimize the performance, where the input is the historical traffic situation while the output is the balanced scheduling solution. E2LB can guarantee the real-time scheduling, since the calling of a well-trained neural network only requires a small number of simple operations. Experiments on the trajectories of taxis and buses in Beijing demonstrate the efficiency and effectiveness of E2LB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".