Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines
Bibliographic record
Abstract
Location-agnostic content delivery, in-network caching, and native support for multicast, mobility, and security are key features of the novel named data networks (NDN) paradigm. NDNs are ideal for hosting content-centric next-generation applications such as Internet of things (IoT) and virtual reality. Intent-driven management is poised to enhance the performance of the offered NDN services to these applications while reducing its management complexity. This article proposes I2DN, intent-driven NDN, a novel architecture that aims at realizing the first step towards intent modeling and mapping to data-plane configurations for NDNs. In I2DN, network operators and application developers express their abstract and declarative content delivery and network service goals and constraints using uttered or written intents. The intents are classified using built-in intent templates, and a slot filling procedure identifies the semantics of the intent. We then employ Event-B machine (EBM) language modeling to represent these intents and their semantics. The resulting EBMs are then gradually refined to represent configurations at the NDN programmable data-plane. The advantages of the proposed adoption of EBM modeling are twofold. First, EBMs accurately capture the desired behavior of the network in response to the specified intents and automatically refine it into concrete configurations. Second, EBM’s formal verification property, referred to as its proof obligation, ensures that the desired properties of the network or its services, as defined by the intent, remain satisfied by the refined EBM representing the final data-plane configurations. Experimental evaluation results demonstrate the feasibility and efficiency of our proposed work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".