Network Slice Lifecycle Management for 5G Mobile Networks: An Intent-Based Networking Approach

Abbas, Khizar; Khan, Talha Ahmed; Afaq, Muhammad; Song, Wang‐Cheol

doi:10.1109/access.2021.3084834

Cited by 68 publications

(20 citation statements)

References 36 publications

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“…Other authors have focused on the demonstration of intent based platforms operating over 5G research infrastructures. For example, [18] and [19] introduce intent based platform prototypes aiming at simplifying the lifecycle management of 5G network slices. The authors in [18] use a 5G stack based on Open Source MANO (OSM) [20] and FlexRAN [21], while in [19] the authors use a stack based on M-CORD [22].…”

Section: B Related Workmentioning

confidence: 99%

NLP Powered Intent Based Network Management for Private 5G Networks

et al. 2023

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Intent driven networking holds the promise of simplifying network operations by allowing operators to use declarative, instead of imperative, interfaces. Adoption of this technology for 5G and beyond networks is however still in its infancy, where the required architectures, platforms, interfaces and algorithms are still being discussed. In this work, we present the design and implementation of a novel intent based platform for private 5G networks powered by a Natural Language Processing (NLP) interface. We demonstrate how our platform simplifies network operations in three relevant private network use cases, including: i) an intent based slice provisioning use case, ii) an intent based positioning use case, and iii) an intent based service deployment use case. Finally, all use cases are benchmarked in terms of intent provisioning time.

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Section: B Related Workmentioning

confidence: 99%

NLP Powered Intent Based Network Management for Private 5G Networks

et al. 2023

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“…The majority of existing solutions in the Literature for intent to network configuration focus on the direct mapping of intents into policies [11], [32]. However, one of the main limitations of this approach is that the rigid modeling of policies as events-condition-actions fails to capture intent goals except in the context of predefined services such as network slicing [40]. A different approach is used in [34] where intents are translated directly into optimization problems for resource assignment and allocation.…”

Section: B Related Approaches For Intent Modeling and Translationmentioning

confidence: 99%

Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines

2022

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Location-agnostic content delivery, in-network caching, and native support for multicast, mobility, and security are key features of the novel named data networks (NDN) paradigm. NDNs are ideal for hosting content-centric next-generation applications such as Internet of things (IoT) and virtual reality. Intent-driven management is poised to enhance the performance of the offered NDN services to these applications while reducing its management complexity. This article proposes I2DN, intent-driven NDN, a novel architecture that aims at realizing the first step towards intent modeling and mapping to data-plane configurations for NDNs. In I2DN, network operators and application developers express their abstract and declarative content delivery and network service goals and constraints using uttered or written intents. The intents are classified using built-in intent templates, and a slot filling procedure identifies the semantics of the intent. We then employ Event-B machine (EBM) language modeling to represent these intents and their semantics. The resulting EBMs are then gradually refined to represent configurations at the NDN programmable data-plane. The advantages of the proposed adoption of EBM modeling are twofold. First, EBMs accurately capture the desired behavior of the network in response to the specified intents and automatically refine it into concrete configurations. Second, EBM's formal verification property, referred to as its proof obligation, ensures that the desired properties of the network or its services, as defined by the intent, remain satisfied by the refined EBM representing the final data-plane configurations. Experimental evaluation results demonstrate the feasibility and efficiency of our proposed work.

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“…By deploying the MEC, the smart devices can move their computationally demanding tasks to the nearest powerful edge servers, conserving energy and lowering latency. Moreover, network function virtualization (NFV) and software-defined networking (SDN) enhanced the MEC capabilities by enabling the deployment of virtual network functions (VNF) on edge [4][5][6], for example, pilot and control functions of UAVs. Recent research on MEC has focused on mobile edge servers rather than fixed servers because they can offer cost-effective, highly flexible, and efficient computation services in a challenging environment.…”

Section: Introductionmentioning

confidence: 99%

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

Khan¹,

Zhang²,

Ahmad³

et al. 2023

Computers, Materials &Amp; Continua

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The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer's a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoSaware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedypolicy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

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Network Slice Lifecycle Management for 5G Mobile Networks: An Intent-Based Networking Approach

Cited by 68 publications

References 36 publications

NLP Powered Intent Based Network Management for Private 5G Networks

NLP Powered Intent Based Network Management for Private 5G Networks

Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

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