{"title":"Intent-based AI system in packet-optical networks towards 6G [Invited]","authors":"Paola Iovanna;Marzio Puleri;Giulio Bottari;Fabio Cavaliere","doi":"10.1364/JOCN.514890","DOIUrl":null,"url":null,"abstract":"This paper presents an intelligent dynamic network optimization system for packet-optical transport networks as the industry moves towards 6G. Such a system leverages specific artificial intelligence techniques to dynamically manage the transport network, optimize resource allocation, and guarantee quality of services. A predictive and adaptive Markov decision process is defined by exploiting an ad hoc model of optical-packet nodes and network representation used for the environment description. Comparison of statistical and neural network-based approaches is done for traffic forecasting. QL, DQL, and PPO are compared to solve the reinforcement learning problem. Challenges and opportunities of applying this system in various scenarios are discussed, and assessment is done by simulations that showed advantages in the following aspects: minimization of bandwidth usage guaranteeing quality of services with respect to a conventional system, improvement of optical offload improvement to reduce power consumption and packet processing, and efficient load balancing.","PeriodicalId":50103,"journal":{"name":"Journal of Optical Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Optical Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10492449/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents an intelligent dynamic network optimization system for packet-optical transport networks as the industry moves towards 6G. Such a system leverages specific artificial intelligence techniques to dynamically manage the transport network, optimize resource allocation, and guarantee quality of services. A predictive and adaptive Markov decision process is defined by exploiting an ad hoc model of optical-packet nodes and network representation used for the environment description. Comparison of statistical and neural network-based approaches is done for traffic forecasting. QL, DQL, and PPO are compared to solve the reinforcement learning problem. Challenges and opportunities of applying this system in various scenarios are discussed, and assessment is done by simulations that showed advantages in the following aspects: minimization of bandwidth usage guaranteeing quality of services with respect to a conventional system, improvement of optical offload improvement to reduce power consumption and packet processing, and efficient load balancing.
期刊介绍:
The scope of the Journal includes advances in the state-of-the-art of optical networking science, technology, and engineering. Both theoretical contributions (including new techniques, concepts, analyses, and economic studies) and practical contributions (including optical networking experiments, prototypes, and new applications) are encouraged. Subareas of interest include the architecture and design of optical networks, optical network survivability and security, software-defined optical networking, elastic optical networks, data and control plane advances, network management related innovation, and optical access networks. Enabling technologies and their applications are suitable topics only if the results are shown to directly impact optical networking beyond simple point-to-point networks.