Bibliographic record
Abstract
Cloud-based multimedia application has emerged as a popular service, delivering on demand media computing and storage to millions of users. Though widely deployed, the quality of service (QoS) in current cloud-based multimedia service is not satisfying, due to the varying user demands and strict response time requirements. This thesis investigates resource allocation approaches to improve QoS for cloud-based multimedia services. A service model is desired to quantify the user demands and resource allocation. To meet this need, we propose a queueing model to characterize the cloud service process, based on which we investigate the response time minimization problem and the resource cost minimization problem in single-service scenario, multi-service scenario, and priority service scenario, respectively. Dynamic workload causes the unbalanced resource utilization and local congestion in multimedia cloud. To address this issue, we propose a two-time-scale resource configuration (TRC) scheme to dynamically allocate virtual machines (VMs) to adapt to varying workload. Based on the TRC scheme, we solve the optimal VM configuration problems to minimize the resource cost or minimize the average response time for the single-site cloud scenario and the multi-site cloud scenario, respectively. We propose optimal workload scheduling schemes at user level and task level, respectively. At user level, we optimize the workload assignment to minimize the response time or minimize the resource cost. At task level, we introduce a directed acyclic graph to model the precedence constraints among tasks, and then solve the execution time minimization problems for sequential structure, parallel structure, and mixed structure, respectively. Cloud gaming is an emerging interactive multimedia service. However, current cloud gaming suffers from a high bandwidth consumption and a large response delay. We propose a hybrid streaming framework to provide a high quality cloud gaming experience. We solve the delay-rate-distortion (d-R-D) optimization problem to minimize the overall distortion under the bandwidth and response delay constraints.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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".