Low-Variance Latency Through Forward Error Correction on Wide-Area Networks
Bibliographic record
Abstract
High bandwidth-delay product (BDP) networks present many performance challenges. We present the design, implementation, and evaluation of UDT+FEC, a software system that provides high throughput, low latency, and low-variance latency on wide-area networks (WAN), especially for large data transfers. Using 2D-XOR forward error correction (FEC), implemented as an extension of the UDP-based Data Transfer (UDT) system, we show that it is possible to match the throughput and latency of other state-of-the-art tools, but also provide the added property of lower variance in the interarrival times of messages at the receiver. With a variety of macro- and micro-benchmarks, we quantify the relative advantages of UDT+FEC, when compared to using GridFTP combined with CUBIC, BBR, and parallel streams.We find that UDT+FEC has substantially lower variance in the latency of message arrivals (approximated by message interarrival times, for one-way transmissions) when compared to different combinations of CUBIC, BBR, and parallel streams.
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.
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 | 0.000 | 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".