J. Baranda, J. Mangues‐Bafalluy, E. Zeydan, L. Vettori, R. Martínez, Xi Li, Andres Garcia-Saavedra, C. Chiasserini, C. Casetti, Konstantin Tomakh, O. Kolodiazhnyi, C. Bernardos
{"title":"On the Integration of AI/ML-based scaling operations in the 5Growth platform","authors":"J. Baranda, J. Mangues‐Bafalluy, E. Zeydan, L. Vettori, R. Martínez, Xi Li, Andres Garcia-Saavedra, C. Chiasserini, C. Casetti, Konstantin Tomakh, O. Kolodiazhnyi, C. Bernardos","doi":"10.1109/NFV-SDN50289.2020.9289863","DOIUrl":null,"url":null,"abstract":"The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1–2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).","PeriodicalId":283280,"journal":{"name":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"122","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NFV-SDN50289.2020.9289863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 122
Abstract
The automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1–2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).