Lower-Latency Screen Updates over QUIC with Forward Error Correction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are workloads that do not need the total data ordering enforced by the Transmission Control Protocol (TCP). For example, Virtual Network Computing (VNC) has a sequence of pixel-based updates in which the order of rectangles can be relaxed. However, VNC runs over the TCP and can have higher latency due to unnecessary blocking to ensure total ordering. By using Quick UDP Internet Connections (QUIC) as the underlying protocol, we are able to implement a partial order delivery approach, which can be combined with Forward Error Correction (FEC) to reduce data latency. Our earlier work on consistency fences provides a mechanism and semantic foundation for partial ordering. Our new evaluation on the Emulab testbed, with two different synthetic workloads for streaming and non-streaming updates, shows that our partial order and FEC strategy can reduce the blocking time and inter-delivery time of rectangles compared to total delivery. For one workload, partially ordered data with FEC can reduce the 99-percentile message-blocking time to 0.4 ms versus 230 ms with totally ordered data. That workload was with 0.5% packet loss, 100 ms Round-Trip Time (RTT), and 100 Mbps bandwidth. We study the impact of varying the packet-loss rate, RTT, bandwidth, and CCA and demonstrate that partial order and FEC latency improvements grow as we increase packet loss and RTT, especially with the emerging Bottleneck Bandwidth and Round-Trip propagation time (BBR) congestion control algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it