HifiCNet: High-Fidelity Cloud Network Validation Platform at Scale by Hybrid Architecture
Bibliographic record
Abstract
Ensuring reliable operation of cloud networks is critical for cloud service providers to guarantee quality of service for tenants. A promising solution is to design a high-fidelity cloud network validation platform that proactively validates the correctness of all operations before implementing changes to the production network. However, the tight coupling between physical and virtual networks in the cloud poses challenges to achieving high-fidelity cloud network validation. Existing network validation platforms focus primarily on traditional physical networks, while ignoring virtual network validation. Regrettably, neglecting the combined validation of physical and virtual networks will result in inaccurate evaluations. To bridge this gap, we present HifiCNet, a high-fidelity platform that concurrently validates both physical and virtual networks. HifiCNet designs an orchestrator to elegantly coordinate the interaction between physical and virtual networks in the cloud and innovatively adopts an emulator-simulator hybrid architecture to ensure high fidelity and scalability for cloud network validation. Through extensive evaluation based on real topologies and traffic traces, we show that HifiCNet enables high-fidelity validation of cloud network configurations, services, and exceptions. Notably, HifiCNet can use 38 servers to establish a physical network comprising 10k hosts, and a virtual network consisting of 200 k virtual machines.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".