Machine learning-based zero-touch network and service management: a survey

Gallego-Madrid, Jorge; Sánchez-Iborra, Ramón; Ruíz, Pedro Ortega; Skarmeta, Antonio F.

doi:10.1016/j.dcan.2021.09.001

Cited by 58 publications

(40 citation statements)

References 71 publications

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“…However, the rest of the paper is focused only on cross-domain ZSM approaches and security aspects. The most similar to this work is [49], in which the authors provide an overview of the ZSM vision, including the review of the ETSI ZSM and the ETSI ENI architectures, as well as an analysis of the contributions provided by European projects in terms of ZSM. The core of the paper provides a high-level set of 7 ML algorithms (i.e., logistic regression, random forest, neural networks, Support Vector Machines (SVM), naive Bayes, k-nearest neighbor, and RL), and links them with four network management functions, namely RAN management, resource management, flow inspection, and multi-domain management.…”

Section: Our Surveymentioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

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Section: Our Surveymentioning

confidence: 99%

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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“…The use of FL for the next-generation networked industrial systems [8], ultra-reliable low-latency vehicular communications [9] presents the significance of FL for future networks. AI act as an enabling technology that improves ZSM performance [10]. Despite being an enabler for ZSM, AI introduces new limitations and risks that need to be addressed to make ZSM a reality [11].…”

Section: Related Workmentioning

confidence: 99%

Robust and Resilient Federated Learning for Securing Future Networks

Siriwardhana

Porambage

Liyanage

et al. 2022

2022 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

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Machine Learning (ML) and Artificial Intelligence (AI) techniques are widely adopted in the telecommunication industry, especially to automate beyond 5G networks. Federated Learning (FL) recently emerged as a distributed ML approach that enables localized model training to keep data decentralized to ensure data privacy. In this paper, we identify the applicability of FL for securing future networks and its limitations due to the vulnerability to poisoning attacks. First, we investigate the shortcomings of state-of-the-art security algorithms for FL and perform an attack to circumvent FoolsGold algorithm, which is known as one of the most promising defense techniques currently available. The attack is launched with the addition of intelligent noise at the poisonous model updates. Then we propose a more sophisticated defense strategy, a threshold-based clustering mechanism to complement FoolsGold. Moreover, we provide a comprehensive analysis of the impact of the attack scenario and the performance of the defense mechanism.

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“…To capitalize on such trends, communication service providers (CSP) need to expand and modernize their methods to deploy and operate networks and services. Traditionally, service design, deployment and operations are human-driven supported by automated scripts and performance monitoring functionalities [4]. System/network architects/engineers understand the service requirements of a user/tenant and follow proprietary processes to deploy the service by performing the required changes in software and hardware resources.…”

Section: Introductionmentioning

confidence: 99%

Intent Negotiation Framework for Intent-driven Service Management

Sharma¹,

Bhamare²,

Kassler³

et al. 2023

Preprint

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<p>To automate network operations and compute ser- vices, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as <em>i</em>ntents. To fulfill the intents, closed control-loop operations perform required configurations and deployments without human intervention. Despite the fact that the intents are fulfilled automatically, conflicts may arise between users and service providers due to limited capabilities of service providers and user requirements specified as intents. This triggers IDSM system to initialize an intent negotiation process among conflicting actors. Intent negotiation involves generating one or more alternate intents based on the current state of the underlying physical/virtual resources, which are then presented to the intent creator for acceptance or rejection. In this way, the quality of services (QoS) can be improved significantly by maximizing the acceptance rate of service requests in the scenario of limited resources. However, intent negotiation sub-systems are still in their infancy. The available solutions are platform dependent which pose various challenges in their adoption to diverse platforms. The main focus of this work has been to draft a comprehensive and generic intent negotiation framework which can be used across diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for a comprehensive intent negotiation framework. A generic intent negotiation framework is then presented incorporating all the interactions among all the identified processes while conflicting actors engage in the intent negotiation, towards the fulfilment of the given service. </p>

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Machine learning-based zero-touch network and service management: a survey

Cited by 58 publications

References 71 publications

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Robust and Resilient Federated Learning for Securing Future Networks

Intent Negotiation Framework for Intent-driven Service Management

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