Virtual Networks Link-Layer Topologies Discovery through Host-based Tracing
Bibliographic record
Abstract
The network link-layer topology, also known as the physical topology, describes the networking hardware devices, their corresponding placement, and the interconnection between them. Discovering and maintaining updated information about the network topology is of utmost importance for many protocols and monitoring tools. For instance, the algorithms of most routing protocols include modules in charge of the automatic discovery of the link-layer topology. On the other hand, network virtualization is one of the technologies that enable Virtual Machines (VMs) in a cloud platform to share the same physical network infrastructure. This technology has been one of the most important key players in the Cloud Computing industry. Unfortunately, research communities have put a modest effort into developing efficient algorithms capable of discovering the topology of Virtual Networks (VNs).In this paper, we address this issue and propose an approach to recover the link-layer topology of a VN from an execution trace. Our approach uses tracing techniques to collect data from the kernels of host machines. Since we limit the data collection only to the host machine, this approach is completely transparent to VMs. Furthermore, we propose a prototype framework that implements our approach.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".