Priority-Based Cloud Computing Architecture for Multimedia-Enabled Heterogeneous Vehicular Users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent days, vehicles have been equipped with smart devices that offer various multimedia-related applications and services, such as smart driving assistance, traffic congestions, weather forecasting, road safety alarms, and many entertainment and comfort applications. Thus, these smart vehicles produce a large amount of multimedia-related data that require fast and real-time processing. However, due to constrained computing and storage capacities, such huge amounts of multimedia-related data cannot be processed in on-board standalone devices. Thus, multimedia cloud computing (MCC) has emerged as an economical and scalable computing technology that can process multimedia-related data efficiently while providing improved Quality of Service (QoS) to vehicular users from anywhere, at any time and on any device, at reduced costs. However, there are certain challenges, such as fast service response time and resource cost optimization, that can severely affect the performance of the MCC. Therefore, to tackle these issues, in this paper, we propose a dynamic priority-based architecture for the MCC. In the proposed scheme, we divide multimedia processing into four different subphases, while computing resources to each computing server are assigned dynamically, according to the workload, in order to process multimedia tasks according to the multimedia user Quality of Experience (QoE) requirements. The performance of the proposed scheme is evaluated in terms of service response time and resource cost optimization using the CloudSim simulator.
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.
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 it