RSU cloud and its resource management in support of enhanced vehicular applications
Bibliographic record
Abstract
We propose Roadside Unit (RSU) Clouds as a novel way to offer non-safety application with QoS for VANETs. The architecture of RSU Clouds is delineated, and consists of traditional RSUs and specialized micro-datacenters and virtual machines (VMs) using Software Defined Networking (SDN). SDN offers the flexibility to migrate or replicate virtual services and reconfigure the data forwarding rules dynamically. However, frequent changes to service hosts and data flows not only result in degradation of services, but are also costly for service providers. In this paper, we use Mininet to analyze and formally quantify the reconfiguration overhead. Our unique RSU Cloud Resource Management (CRM) model jointly minimizes reconfiguration overhead, cost of service deployment and infrastructure routing delay. To the best of our knowledge, we are the first to utilize this approach. We compare the performance of purist approach to our Integer Linear Programming (ILP) model and our innovative heuristic for the CRM technique and discuss the results. We will show the benefits of a holistic approach in Cloud Resource Management with SDN.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".