An Intelligent and Programmable Data Plane for QoS-Aware Packet Processing
Bibliographic record
Abstract
One of the main features of data plane programmability is that it allows the easy deployment of a programmable network traffic management framework. One can build an early-stage Internet traffic classifier to facilitate effective Quality of Service (QoS) provisioning. However, maintaining accuracy and efficiency (i.e., processing delay/pipeline latency) in early-stage traffic classification is challenging due to memory and operational constraints in the network data plane. Additionally, deploying network-wide flow-specific rules for QoS leads to significant memory usage and overheads. To address these challenges, we propose new architectural components encompassing efficient processing logic into the programmable traffic management framework. In particular, we propose a single feature-based traffic classification algorithm and a stateless QoS-aware packet scheduling mechanism. Our approach first focuses on maintaining accuracy and processing efficiency in early-stage traffic classification by leveraging a single input feature - sequential packet size information. We then use the classifier to embed the Service Level Objective (SLO) into the header of the packets. Carrying SLOs inside the packet allows QoS-aware packet processing through admission control-enabled priority queuing. The results show that most flows are properly classified with the first four packets. Furthermore, using the SLO-enabled admission control mechanism on top of the priority queues enables stateless QoS provisioning. Our approach outperforms the classical and objective-based priority queuing in managing heterogeneous traffic demands by improving network resource utilization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".