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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18717877 |
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| _version_ | 1866901546851106816 |
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| author | Karamchandani Batra, Amit González-Sánchez, Daniel de la Cal García, Luis Bellido Triana, Luis Mozo Velasco, Bonifacio Alberto Lentisco, Carlos M. De la Osa Mostazo, David Pastor Perales, Antonio R. Lopez, Diego |
| author_facet | Karamchandani Batra, Amit González-Sánchez, Daniel de la Cal García, Luis Bellido Triana, Luis Mozo Velasco, Bonifacio Alberto Lentisco, Carlos M. De la Osa Mostazo, David Pastor Perales, Antonio R. Lopez, Diego |
| contents | This repository provides the ML inference engine for the ACROSS TC3.2 use case (Smart Energy-aware Zero-touch Traffic Engineering). The software predicts router power consumption from instantaneous network telemetry-derived metrics to support Network Digital Twin (NDT) environments and energy-aware traffic engineering. The inference service is containerized (Docker/Docker Compose) and uses Kafka for message exchange: it consumes input telemetry features, runs a selected ML model, and publishes predicted power consumption along with feature-based variation rates. The repository also includes a telemetry mock service to replay experiment datasets, validate predictions against ground truth, and generate verification plots. Supported router types: RA and RB. Supported model types: linear regression, polynomial regression, random forest, and deep neural networks (with optional scaling). Models are organized by router type/model type and selected dynamically via environment configuration. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18717877 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TC3.2 Smart Energy-aware Zero-touch Traffic Engineering – ML Inference Engine (TC32_ML) Karamchandani Batra, Amit González-Sánchez, Daniel de la Cal García, Luis Bellido Triana, Luis Mozo Velasco, Bonifacio Alberto Lentisco, Carlos M. De la Osa Mostazo, David Pastor Perales, Antonio R. Lopez, Diego energy efficiency network telemetry Traffic Engineering Network Digital Twin Artificial Intelligence machine learning Kafka Docker routers zero-touch This repository provides the ML inference engine for the ACROSS TC3.2 use case (Smart Energy-aware Zero-touch Traffic Engineering). The software predicts router power consumption from instantaneous network telemetry-derived metrics to support Network Digital Twin (NDT) environments and energy-aware traffic engineering. The inference service is containerized (Docker/Docker Compose) and uses Kafka for message exchange: it consumes input telemetry features, runs a selected ML model, and publishes predicted power consumption along with feature-based variation rates. The repository also includes a telemetry mock service to replay experiment datasets, validate predictions against ground truth, and generate verification plots. Supported router types: RA and RB. Supported model types: linear regression, polynomial regression, random forest, and deep neural networks (with optional scaling). Models are organized by router type/model type and selected dynamically via environment configuration. |
| title | TC3.2 Smart Energy-aware Zero-touch Traffic Engineering – ML Inference Engine (TC32_ML) |
| topic | energy efficiency network telemetry Traffic Engineering Network Digital Twin Artificial Intelligence machine learning Kafka Docker routers zero-touch |
| url | https://doi.org/10.5281/zenodo.18717877 |