An Adaptive Traffic-Flow based Controller Deployment Scheme for Software-Defined Vehicular Networks
Bibliographic record
Abstract
Software-Defined Vehicular Networks has been a vital component for heterogeneous radio access technologies to support massive data load through various safety and infotainment applications. Elevating the constraint of static hardware network devices into a programmable unit and providing a global view of the network status and standard interface between heterogeneous radio access technologies. However, having a logically centralized control unit brings several challenges, including bottleneck problem and densification issues. A distributed control plane comes as a possible solution to the centralized control plane yet with several questions of where to deploy the control units and how many SDN controllers are needed in a given network structure. In this paper, we present an adaptive Flow-based controller deployment and assignment strategy for distributed Software-Defined Vehicular Networks through the utilization of the communication latencies between switch-enabled access points and their corresponding vehicles' flow over a time window. We evaluate the proposed method's performance in terms of end-to-end delay and load on the resulted controller's points and their cluster's set. The clustering method is compared to several types of static placement strategies, in which the proposed method showed a reduction in controllers' average delays while distributing the load among them over time.
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 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) | 0.001 | 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 | 0.000 |
| 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".