Adaptive distributed compression technique utilizing intermediate network nodes
Bibliographic record
Abstract
Data networks are experiencing an unprecedented growth in traffic resulting in a multitude of challenges including high network congestion, latency, and packet loss rates. These challenges greatly affect the performance of the network and have a drastic impact on the Quality of Service (QoS) for various applications. In order to reduce network congestion and in turn, maintain the desired QoS, an adaptive and distributed compression/decompression scheme is proposed. The proposed scheme performs the compression and decompression processes within the queues of the intermediate routing nodes of a network. The payload of a packet is compressed based on the packet queuing time. The average expected queuing time of packets is determined instantaneously within the intermediate nodes of a network by employing the Pollaczek Khinchin (PK) equation. Using Network Simulator 3 (NS3), the proposed scheme was simulated under different network conditions and compared with non-compressed transmission sessions of different flow rates. The simulation results show a dramatic improvement in network performance. More specifically, the number of lost packets and one way latency are reduced by at least 22.50% and 18%, respectively.
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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 | 0.000 |
| Open science | 0.001 | 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".