MD-IDN: Multi-domain intent-driven networking in software-defined infrastructures
Bibliographic record
Abstract
Intent-Driven Networking is recently gaining interest, with all major SDN control platforms now providing an intent Northbound Interface (NBI) as a high-level abstraction for network management. With these frameworks network operators can conveniently define “what needs to be done”, rather than “how it should be done”. Current IDN frameworks pose two main limitations that affect deployment in production grade and multi-domain networks. They are mainly concerned with a single network domain, and thus enabling end-to-end network intents over a multi-domain and large-scale setup is still a challenge. Furthermore, these frameworks do not consider any differentiation between user intents and provider intents, and a limited set of intent classes are available for both. In this paper we present MD-IDN, which provides an intent framework for the users of multi-domain cloud infrastructures. We first propose a graph-based abstraction model for user-defined intents and a generic intent compilation process. Then, we propose compilation algorithms to achieve scalability in multi-domain networks: First, user-defined intents get processed over an abstracted multi-graph of network domains and their interconnections, and a set of local intents will be generated for each of the involved domains. Afterwards, the local intents will be compiled and installed in local regions in parallel. MD-IDN is deployed as a public service in the SAVI Testbed over more than ten data centers spanning across Canada. In multi-domain environments, our experiments show that MD-IDN outperforms current practices that compile intents over a flat network topology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".