DeeTune: Design and Application of an eBPF-based Network Framework for Baidu
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it