Reliable Cybertwin-Driven Concurrent Multipath Transfer With Deep Reinforcement Learning
Bibliographic record
Abstract
It is well known that concurrent multipath transfer (CMT) can improve the transmission rate. However, due to multiple heterogeneous paths from users to the access network, a large number of out-of-order packets significantly degrade the overall transmission reliability. Cybertwin provides a potential solution to alleviate the packet out-of-order problem by accurately detecting and perceiving the path state. In this article, we investigate the data scheduling problem and propose a learning-based cybertwin-driven CMT algorithm to obtain the optimal data scheduling policy. In particular, we first formulate the data scheduling problem as an integer linear programming by taking the QoS metrics into account. To cope with the packet out-of-order problem in CMT, we propose a reliable cybertwin-CMT with deep reinforcement learning (CMT-DRL) algorithm to determine the data scheduling decisions. The proposed algorithm takes multipath throughput, end-to-end delay, and packet loss rate into account. Besides, CMT-DRL adopts an asynchronous learning framework to efficiently execute data collection, packet scheduling, and neural network training in sequence by decoupling model training and execution. We conduct extensive experiments in a P4-based programmable network platform. Experimental results indicate that the CMT-DRL outperforms the existing benchmarks in terms of the number of out-of-order packets, round-trip time, and throughput.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".