DeeTune: Design and Application of an eBPF-based Network Framework for Baidu
Bibliographic record
Abstract
With the development of cloud computing and the continuous development of infrastructure, architecture upgrades and other technologies, Baidu's internal services are gradually moving to the cloud environment. Although the efficiency of services has increased significantly, some shortcomings and deficiencies of the basic capabilities of the cloud environment have gradually become apparent, resulting in the inability to meet some reasonable requirements of the enterprise, such as building the topology relationship between different microservices and conducting the test session for The traditional way of implementation is to record the real traffic to reflect and verify the function and so on. The traditional way of implementation is often to implant the code into the business system to make changes. However, given the diversity of business forms and technologies, the conventional way has a lot of problems in terms of business intervention, communication and coordination, performance, stability, and other aspects. In this paper, we introduce Baidu's eBPF-based network framework: DeeTune, which provides the ability to create service topology, record traffic, monitor non-intrusive metrics, etc., further improve the efficiency of SRE and quality assurance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".