Quality of service evaluations of multicast streaming protocols
Bibliographic record
Abstract
Recently proposed scalable on-demand streaming protocols have previously been evaluated using a system cost measure termed the "required server bandwidth". For the scalable protocols that provide immediate service to each client when the server is not overloaded, this paper develops simple analytic models to evaluate two client-oriented quality of service metrics, namely (1) the mean client waiting time in systems where clients are willing to wait if a (well-provisioned) server is temporarily overloaded, and (2) the fraction of clients who balk (i.e., leave without receiving their requested media content) in systems where the clients will tolerate no or only very low service delays during a temporary overload. The models include novel approximate MVA techniques that appear to extend the range of applicability of customized AMVA to include questions focussed on state probabilities rather than on mean values, and to systems in which the operating points of interest do not include substantial client queues. For example, the new AMVA models accurately estimate the server bandwidth needed to achieve a balking rate as low as one in ten thousand. The analytic models can easily be applied to determine the server bandwidth needed for a given number of media files, anticipated total client request rate and file access frequencies, and target balking rate or mean wait. Results show that (a) scalable media servers that are configured with the "required server bandwidth" defined in previous work have low mean wait but may have unacceptably high client balking rates (i.e., greater than one in twenty), (b) for high to moderate client load, only a 10 - 50% increase in the previously defined required server bandwidth is needed to achieve a very low balking rate (e.g., one in ten thousand), and (c) media server performance (either mean wait or balking rate) degrades rapidly if the actual client load is more than 10% greater than the anticipated load.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".