Petri Net-Based Model for 5G and Beyond Networks Resilience Evaluation

Li, Rui; Decocq, Bertr; Barros, Anne; Fang, Yiping; Zeng, Zhiguo

doi:10.1109/icin53892.2022.9758134

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…As a solution, some backup-sharing strategies are implemented with the support of an SPN model of virtualized blocks. Timed Stochastic Colored Petri Nets have been employed in [26] to analyze the resiliency of a 5G infrastructure by dividing it into the main principal components (the physical infrastructure, the virtual infrastructure and the network services). Again, Generalized Stochastic Petri Nets have been employed in [27] to face availability problems in data centers in charge of managing SFCs.…”

Section: Related Workmentioning

confidence: 99%

Performance and Availability Challenges in Designing Resilient 5G Architectures

Simone,

Mauro,

Natella

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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This work proposes a stochastic characterization of resilient 5G architectures, where attributes such as performance and availability play a crucial role. As regards performance, we focus on the delay associated with the Packet Data Unit session establishment, a 5G procedure recognized as critical for its impact on the Quality of Service and Experience of end-users. To formally characterize this aspect, we employ the non-productform queueing networks framework where: 𝑖) main nodes of a 5G architecture have been realistically modeled as 𝐺/𝐺/𝑚 queues which do not admit analytical solutions; 𝑖𝑖) the decomposition method useful to catch subtle quantities involved in the chain of 5G interconnected nodes has been conveniently customized. The results of performance characterization constitute the input of the availability modeling, where we design a hierarchical scheme to characterize the probabilistic failure/repair behavior of 5G nodes combining two formalisms: 𝑖) the Reliability Block Diagrams, useful to capture the high-level interconnections between nodes; 𝑖𝑖) the Stochastic Reward Networks to model the internal structure of each node. The final result is an optimal resilient 5G setting that fulfills both a performance constraint (e.g., a temporal threshold) and an availability constraint (e.g., the so-called five nines) at the minimum cost, namely, with the smallest number of redundant elements. The theoretical part is complemented by an empirical assessment carried out through Open5GS, a 5G testbed that we have deployed to realistically estimate main performance and availability metrics.

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Section: Related Workmentioning

confidence: 99%

Performance and Availability Challenges in Designing Resilient 5G Architectures

Simone,

Mauro,

Natella

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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“…We use Petri Net to model the 5G system. Readers are invited to refer to Li et al (2022), our previous work, for more details on the Petri Net model.…”

Section: Petri Net-based Model For 5g System Descriptionmentioning

confidence: 99%

A Petri Net-based model to Study the Impact of Traffic Changes on 5G Network Resilience

Li¹,

Decocq²,

Fang³

et al. 2022

Book of Extended Abstracts for the 32nd European Safety and Reliability Conference

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The advent of 5G has enabled a wide variety of devices to access the network. With the digitization of industry, more and more vertical services, such as smart cities, remote health, and autonomous driving, rely on 5G networks for communication. These verticals bring new challenges to the telecommunication network resilience. Among them, sudden traffic change seems to be a critical challenge that impacts the resilience performance of 5G networks. This paper presents a network model for future 5G infrastructures based on Petri nets by taking into consideration the particularities of network virtualization and softwarization. This work also seeks to analyze the effectiveness of microservice-level autoscaling and network isolation by using discrete event simulation. The results suggest that both autoscaling and network isolation could increase network resilience when network traffic changes abruptly.

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