A Parallel Data Stream Layer for Large Data Workloads on WANs
Bibliographic record
Abstract
A variety of workloads on wide-area networks (WAN) can benefit from parallel data streams. Whether between public and private clouds or between high-performance computing centres, high bandwidth-delay-product (BDP) networks with even small amounts of packet loss (e.g., due to congestion) can suffer reduced TCP/IP throughput. Therefore, we design, implement, and evaluate the open-source Parallel Data Streams (PDS) user-level tool. PDS makes trade-offs (e.g., TCP fairness) for specific workloads, when performance is the key goal. Unlike the well-known GridFTP, PDS also supports tools such as rsync, git, Virtual Network Computing (VNC), and the Network File System (NFS). We also quantify and contribute a performance evaluation of PDS, using a combination of emulated and real WANs. We establish that PDS achieves comparable performance to GridFTP for file transfer, but additional functionality via other tools. For example, PDS can transfer a 14 GB file on a WAN between Alberta and Quebec (maximum 1 Gbps; over 3,100 km) at 861 Mbps, using rsync and 8 parallel, cleartext, TCP streams. In comparison, rsync over a single SSH stream achieves 274 Mbps.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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".