Enhancement of Data Center Transmission Control Protocol Performance in Network Cloud Environments
Bibliographic record
Abstract
The increasing use of cloud services in several areas has led to the growth of data-intensive applications.It is necessary to find ways to enhance the efficiency of communications within the networks of data centers to improve the performance of cloud environments.Explicit Congestion Notification (ECN) is used by Data Center TCP (DCTCP) to enhance congestion control in data center networks.The DCTCP uses ECN to assess the amount of congestion, whereas normal TCP congestion management can simply detect its presence.This paper examines DCTCP using the Random Early Detection (RED) queue management strategy.The evaluation reveals that employing Random Early Detection incurs certain costs.The RED is criticized on the one hand for both short-and medium-term connections due to longer completion time delays compared to typical DCTCP techniques.Because of ECN, DCTCP may maintain small queue sizes.However, because RED uses the average queue size, it penalizes short-lived traffic because it does not reach the bottleneck quickly.An intelligent queue management mechanism with ECN is believed to enhance DCTCP's performance in a cloud-computing environment by predicting sending rates and providing fast feedback on queue length.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".