Dynamic-Adaptive AI Solutions for Network Slicing Management in Satellite-Integrated B5G Systems

Lei, Lei; Yuan, Yaxiong; Vu, Thang X.; Chatzinotas, Symeon; Minardi, Mario; Montoya, Jesus Fabian Mendoza

doi:10.1109/mnet.111.2100206

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…f (t) is a binary indicator function, which has the value "1" if there is at least one difference between the link mapping at time slot t and the one at time slot t + 1. Based on the previous definition, equation (4) shows the initial formulation of the objective.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…The embedding of different demands onto the same substrate, or physical network, gives a very relevant added-value to the telecom industry-academic sector and has opened the way for the development of those very attracting use-cases of 5G, such as enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC). Currently, network slicing has drawn a considerable attention of research activities, due to its strong impact and benefits [4]. The large field of network virtualization aims at efficiently embedding Virtual Network Requests (VNRs), onto the physical network, which is defined as Virtual Network Embedding (VNE).…”

Section: Introductionmentioning

confidence: 99%

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Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Minardi

Lei

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Mixed Binary Linear Programming (MBLP) formulation for a Dynamic Topology-Aware VNE (DTA-VNE) algorithm. Given priori information about the network's evolution over time, DTA-VNE plans the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. DTA-VNE aims at minimizing this migration cost to avoid unnecessary remappings. To tackle the exponential complexity of the MBLP, we propose an efficient algorithm based on relaxation approaches (DTA-R) to solve large-scale problems. In numerical results, the effectiveness of the proposed DTA-R is demonstrated with much lower migration cost than the conventional implementations. The trade-off between the computation time and migration cost of DTA-R is studied. Finally, we test DTA-R and the baselines in our developed MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT) to precisely quantify the packet lost for each migration. DTA-R proved to reduce the packet lost by ∼ 2.5 − 5% compared to baselines.

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Section: B Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Minardi

Lei

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…Various Machine Learning (ML) [37][38][39] and Deep Learning (DL) [40][41][42][43][44] algorithms are being applied to manage slices, user requests, slice admissions, resources, and traffic in 5G network slicing. Various optimization algorithms [45,46] are also utilized to optimize the network functions.…”

Section: This Surveymentioning

confidence: 99%

ML-Based 5G Network Slicing Security: A Comprehensive Survey

et al. 2022

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Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.

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“…When network parameters vary dramatically, the performance of the learning models can be degraded. To remedy this, one has to re-collect a large number of training data and re-train the learning models, which is time-consuming and inefficient to adapt to fast variations [10].…”

Section: A Related Work: State-of-the-art and Limitationsmentioning

confidence: 99%

“…Following the principles in ( 7)-( 10), we enumerate valid links and candidate groups. In implementation, every enumerated link or group will undergo a feasiblity-check step to ensure that no links or groups violate ( 7)- (10).…”

Section: Optimization Problemmentioning

confidence: 99%

Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling

Yuan

Lei

et al. 2022

IEEE Trans. Wireless Commun.

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Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL's effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

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Dynamic-Adaptive AI Solutions for Network Slicing Management in Satellite-Integrated B5G Systems

Cited by 20 publications

References 14 publications

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

ML-Based 5G Network Slicing Security: A Comprehensive Survey

Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling

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