A Novel Multi-Factor Aware Online Scheduling Method for Improving Vehicular Edge Computing Efficiency
Bibliographic record
Abstract
Vehicular Edge Computing (VEC), as one of the major components of Intelligent Transportation Systems, improves road safety by providing computing services to safety-related applications on vehicles. Currently, the existing fine-grained computing scheduling algorithms are normally designed based on some simple scheduling policies. Due to the heterogeneous nature of tasks offloaded from various applications, they may not effectively satisfy various performance requirements of the real system, thereby leading to the problem that the short-term residual computing power cannot be effectively utilized when computing-costly tasks occupy the server. Therefore, improving the overall system performance and the efficiency of utilizing computing power is a critical issue. Accordingly, in this paper, we study the problem of computing scheduling inside edge servers in VEC, where multiple tasks can be offloaded to Road Side Units (RSUs). We analyze the role played by multiple evaluation metrics in the existing methods for ensuring the quality of service (QoS) and further design a novel online multi-factor aware task offloading algorithm with a hierarchical fine-grained computing scheduling scheme inside the edge server. We evaluate it by conducting intensive simulation tests and comparing the results with some state-of-the-art approaches. Numerical results show that the proposed algorithm outperforms the methods in the control group in different aspects and achieves the best overall performance.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".