Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

Debbabi, F.; Jmal, Rihab; Fourati, Lamia Chaari; Ksentini, Adlen

doi:10.1109/access.2020.3022162

Cited by 39 publications

(25 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Network slicing methods can enhance the quality of service and quality of user experiences to respond to real-time requests through ML, taking into account network dynamics and network changes. Network slicing needs to enlist ML techniques for automated provisioning and proactive management of traffic and services [42]. However, the convergence speed of ML methods is very important because incorrect decisions about resource allocation due to lack of convergence or low convergence speed in the used ML method can cause poor network performance.…”

Section: H Algorithmic Aspects Of Resource Allocationmentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

View full text Add to dashboard Cite

One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

show abstract

Section: H Algorithmic Aspects Of Resource Allocationmentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In new-generation cellular networks, services are provided through the provisioning of logical networks according to the well-known network slicing paradigm, on which useful surveys can be found in [26], [27], [28], [29], [30]. In this context, the MANO architecture [9] is often used for the life cycle management and the runtime operation of network slices.…”

Section: Related Workmentioning

confidence: 99%

ML-Driven Provisioning and Management of Vertical Services in Automated Cellular Networks

Casetti¹,

Chiasserini²,

Marcato³

et al. 2022

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a largescale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

show abstract

“…Several surveys have been already published on the subject of network slicing. A few of them review network slicing architectures and principles [3], [13], [14], while others focus on the algorithmic aspects of network slicing [15], [17] and its common mathematical modeling [16]. Two recent surveys focused on very specific applications of deep reinforcement learning (DRL) in a network slicing context [18], [19].…”

Section: A Scope Of the Surveymentioning

confidence: 99%

“…They analyze the key attributes and functions of the most common models and propose unified network slicing models for efficient end-to-end (E2E) network slicing management. Furthermore, the authors of [14] discuss the potential and integration of multi-access edge computing (MEC) and cloud [3] 5G network slicing architectures [13] Network slice creation models and slicing templates proposed by SDOs [14] 5G network slicing development and its integration with the MEC and the cloud [15] Optimization frameworks for network slicing [16] Mathematical modelling encompassing game theory models, prediction models, failure recovery models in resource allocation methods [17] Algorithmic issues for admission control and resource allocation aspects in network slicing, including RL methods [18] Admission control, resource allocation and resource orchestration aspects in network slicing with DRL approaches [19] DRL-based contributions in network slicing Our Survey ML-based algorithmic approaches in network slicing technologies in network slicing. However, algorithmic aspects of resource management perspectives in network slicing have not been studied in these surveys.…”

Section: A Scope Of the Surveymentioning

confidence: 99%

Machine Learning in Network Slicing - A Survey

Phyu¹,

Naboulsi²,

Stanica³

2022

Preprint

View full text Add to dashboard Cite

<p>5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous<br> requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on<br> ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.</p>

show abstract

Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

Cited by 39 publications

References 72 publications

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

ML-Driven Provisioning and Management of Vertical Services in Automated Cellular Networks

Machine Learning in Network Slicing - A Survey

Contact Info

Product

Resources

About