The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of ML applications in any domain can only be ensured if intelligent capabilities cover all of its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. First, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Second, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/cross-domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned previously be addressed with improved ML architecture and agile operations and propose an ML pipeline based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain, and multi-layer environment and provide decision making on a global or holistic rather than an individual function level.

本研究的背景是通过调查技术检查未来智能网络 (FIN) 的要求、解决方案和当前的研究方向。这项研究的背景取决于机器学习 (ML) 在网络领域的应用。通过对文献和现实世界报告的仔细分析，我们注意到机器学习显着加快了决策过程，增强了智能自动化，并帮助经济地解决了生活不同领域的复杂问题。各种研究人员还设想了将智能功能和操作与 ML 相结合的未来网络。已经做出了一些努力来自动化网络域中的各个功能和操作；然而，文献中提出的大多数现有 ML 模型都缺乏几个重要的要求。因此，本研究旨在全面总结 FIN 的要求，并提出需要配备 ML 技术的不同网络功能的分类法。本研究的核心目标是提供端到端 FIN 设想的要求分类、相关 ML 技术及其分析，以发现研究差距、未解决的问题和未来的研究方向。只有智能功能涵盖其所有组件，才能确保 ML 应用程序在任何领域的真正优势。我们观察到，未来几代网络是异构的、多供应商的、多维的，只有在整体范围内使用智能能力，机器学习才能提供最佳结果。只有当 ML 算法能够解决多供应商和多维环境中的异构问题时，才有可能在整体范围内实现智能。ML 模型必须可靠、高效、支持并具备跨网络层和管理域学习和共享知识以解决问题的能力。首先，本研究确定了 FIN 的要求，并通过对各种研究人员的设想和使用关键字查询从知名会议和标准制定组织收集的文章进行评论，提出了它们的分类法。其次，我们回顾了现有的 ML 应用研究，重点关注覆盖范围、异构性、分布式架构以及跨领域知识学习和共享。我们的研究观察到，在过去，ML 应用程序主要只关注个人/隔离级别，并没有广泛探索具有跨层/跨领域知识共享和敏捷 ML 操作的全局和深度整体学习的各个方面。我们建议通过改进的 ML 架构和敏捷操作来解决前面提到的问题，并为 FIN 提出基于 ML 管道的架构。这项研究的重大贡献是推动研究人员寻找适用于模块化、分布式、多域和多层环境的 ML 模型，并提供全局或整体而不是单个功能级别的决策。我们建议通过改进的 ML 架构和敏捷操作来解决前面提到的问题，并为 FIN 提出基于 ML 管道的架构。这项研究的重大贡献是推动研究人员寻找适用于模块化、分布式、多域和多层环境的 ML 模型，并提供全局或整体而不是单个功能级别的决策。我们建议通过改进的 ML 架构和敏捷操作来解决前面提到的问题，并为 FIN 提出基于 ML 管道的架构。这项研究的重大贡献是推动研究人员寻找适用于模块化、分布式、多域和多层环境的 ML 模型，并提供全局或整体而不是单个功能级别的决策。