Dynamic optimization of readsize in hypermedia servers
Bibliographic record
Abstract
While disk scheduling techniques for continuous media (CM) servers are mostly designed to maximize the number of concurrent CM data streams, considerably less attention has been paid to discrete media (DM) data throughput. DM data throughput is vital to systems that need to support the heterogeneity and variety of data found in interactive hypermedia and digital library applications. Therefore, a server's ability in delivering a high DM data throughput without degrading its CM data throughput deserves more attention. A new technique is introduced to optimize the use of disk bandwidth at run-time and redirect the saved bandwidth to service DM requests. Based on a new cost model of disk access incurred in CM service, we formulate strategies to moderate the size of disk reads at run-time. Through experimental evaluations, these strategies are found to improve the efficiency of disk accesses and the DM data throughput of a hypermedia server without jeopardizing the throughput and quality of CM service.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".