Host-Assisted Transport Layer in Data Centers Using Network-Aware Rate Adjustment
Bibliographic record
Abstract
Next generation applications for datacenters, such as Distributed Machine Learning (DML) and Big Data, have complex communication patterns that demand a scalable, stateless and application-aware optimal transport protocol to maximize network utilization and improve application performance. Recent transport protocols either provide limited benefits due to lack of information sharing between application and network; or implement complex stateful mechanisms to improve the application performance. In this paper, we present Omni- Transport Mechanism (Omni-TM) as a message-based congestion control protocol. Omni-TM allows exchanging message information with the network to negotiate the optimal transmission rate without maintaining a per-flow state at the switches (i.e., stateless). Omni- Tmis designed to reach maximum link capacity in one shot. Our simulation results show that Omni- Tmdemonstrates better traffic control decisions (i.e., close to zero queue length while maintaining high link utilization). Furthermore, Omni- Tmreduces Flow Completion Time (FCT) up to 45 % in a realistic workload compared to DCTCP.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".