Prediction-based resource allocation in clouds for media streaming applications
Bibliographic record
Abstract
Media streaming applications have recently attracted large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is difficult to provide streaming distribution with guaranteed QoS relying only on central resources at the content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., VoD provider) can use to obtain resources on-demand. Since a media content provider is charged for amount of resources (bandwidth) rented from the cloud, an open problem is to decide on the right amount of resources allocated in the cloud and their reservation time such that the financial cost on the content provider is minimized. We consider a practical pricing model that is based on a non-linear tariff (i.e., a pricing scheme that depends non-linearly on the resources purchased or time reserved). We formulate the optimization problem based on prediction of future streaming demand. We then propose a simple (easy to implement) algorithm for resource allocation that exploits the non-linearity in the price contract, while ensuring that sufficient resources is reserved in the cloud without incurring wastage. The results of our numerical evaluation and simulations show that the proposed algorithm mimics the optimum solution very well.
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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 | 0.000 | 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".