Exploiting excessive resources at data-centres of media content providers using cloud computing
Bibliographic record
Abstract
It is widely accepted that cloud computing technologies will soon have substantial impact on a broad range of industrial sectors. For example, media content providers can use the advances in cloud computing technologies to exploit the excessive bandwidth and computing resources available at their data-centres. In cloud computing, resources can be seen as a utility or commodity. Thus, cloud computing creates the possibility for a media content provider to increase its monetary profit by offering (renting out) the idle resources at its data-center to users of other communities. Our contributions in this paper are twofold. Firstly, we introduce our innovative system design that enables the media content provider to exploit the excessive resources available at its data-centre using cloud computing. Secondly, we design admission control algorithm that selects the set of tasks to admit at the data-center such that the monetary profit is maximized; while ensuring that the demand for media streaming capacity by clients of the media content provider can be sustained at any instant of time with some level of confidence in probabilistic sense.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".