Dynamic Virtual Machine Migration in a vehicular cloud
Bibliographic record
Abstract
Vehicular clouds are formed by incorporating cloud-based services into vehicular ad hoc networks. Amongst the several challenges in a vehicular cloud network, virtual machine migration (VMM) may be one of the most crucial issues that need addressing. In this paper, a novel solution for VMM in a vehicular cloud is presented. The vehicular cloud is modeled as a small corporate data center with mobile hosts, equipped with limited computational and storage capacities. The proposed scheme is called Vehicular Virtual Machine Migration (VVMM). The VVMM aims to achieve efficient handling of frequent changes in the data center topology, host heterogeneity, all while doing so with minimum Roadside Unit (RU) intervention. Three modes of VVMM are studied. The first mode, VVMM-U uniformly selects the destinations for VM migrations, which will take place shortly prior to a vehicle's departure from the coverage of the RU. The second mode, VVMM-LW aims at migrating the VM to the vehicle with the least workload, and the third mode, VVMM-MA incorporates mobility awareness by migrating the VM to the vehicle with the least workload and forecasted to be within the geographic boundaries of the vehicular cloud. We evaluate the performance of our proposed framework through simulations. Simulation results show that VVMM-MA introduces significant reduction in unsuccessful migration attempts and results in an increased fairness in vehicle capacity utilization across the vehicular cloud system.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".