Disk Prefetching Mechanisms for Increasing HTTP Streaming Video Server Throughput
Why this work is in the frame
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Bibliographic record
Abstract
Most video streaming traffic is delivered over HTTP using standard web servers. While traditional web server workloads consist of requests that are primarily for small files that can be serviced from the file system cache, HTTP video streaming workloads often service a long tail of large infrequently requested videos. As a result, optimizing disk accesses is critical to obtaining good server throughput. In this article we explore serialized, aggressive disk prefetching, a technique that can be used to improve the throughput of HTTP streaming video web servers. We identify how serialization and aggressive prefetching affect performance, and, based on our findings, we construct and evaluate Libception, an application-level shim library that implements both techniques. By dynamically linking against Libception at runtime, applications are able to transparently obtain benefits from serialization and aggressive prefetching without needing to change their source code. In contrast to other approaches that modify applications, make kernel changes, or attempt to optimize kernel tuning, Libception provides a portable and relatively simple system in which techniques for optimizing I/O in HTTP video streaming servers can be implemented and evaluated. We empirically evaluate the efficacy of serialization and aggressive prefetching both with and without Libception, using three web servers (Apache, nginx, and the userver) running on two operating systems (FreeBSD and Linux). We find that, by using Libception, we can improve streaming throughput for all three web servers by at least a factor of 2 on FreeBSD and a factor of 2.5 on Linux. Additionally, we find that with significant tuning of Linux kernel parameters, we can achieve similar performance to Libception by globally modifying Linux’s disk prefetch behaviour. Finally, we demonstrate Libception’s ability to reduce the completion time of a microbenchmark involving two applications competing for disk resources.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it