A novel architecture for efficient management of multimedia-service clouds
Bibliographic record
Abstract
This paper presents a novel architecture for efficient management of multimedia-service clouds. The aim of the proposed architecture is to provide an optimal yet transparent customer access to specialized media editing, processing and adaptation services over these clouds. To achieve this goal, a hierarchy of virtual service models (VSMs) is built recursively; the leaf nodes of this hierarchy represent commonly accessed primitive services such as media caching, format transformation and color change. At higher levels, more complex services are built recursively using lower level VSMs as their building blocks. Each VSM represents a group of actual services offered on the cloud. A corresponding hierarchical service performance model is maintained to help the cloud broker in assigning appropriate services to requests of cloud customers. The performance model is built by collecting performance measurements from already serviced data streams in order to provide an up-to-date service performance view along the hierarchy. The architecture allows users to specify service requests at various levels of details, then an optimal primitive or a compound virtual service comprised of an optimal chain of virtual media services is selected. Performance results demonstrate the efficiency of the proposed architecture.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".