Bibliographic record
Abstract
The relocation of live, running virtual machines from one physical host to another is a new and very desirable function because it provides a variety of features, including resilience to failures and flexibility of location. The data transfers required for live migration are supported in several commercial products by a protocol called iSCSI (Internet SCSI), which runs on top of TCP. We thoroughly tested the performance of a common open source component, the Open-iSCSI initiator, and found a drastic throughput degradation on 100 Mbps networks where the round trip time was more than about 40 ms. We localized the problem to the TCP send buffer size and tested two methods of setting the TCP send buffer size appropriately. Based on our results, we propose a performance tuning scheme that enables users of Open-iSCSI to achieve significant throughput gains. Our scheme results in a dramatic throughput jump from 14 Mbps to 70 Mbps on a 100 Mbps link with an RTT of 100 ms. We also modified one of the data structures internal to Open-iSCSI to handle multiple memory pages in a single scatter/gather list entry. This modification resulted in an additional 20% throughput increase on a 100 Mbps link with an RTT of 200 ms.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".