An Intelligent Forwarding Strategy in SDN-Enabled Named-Data IoV

Tariq, Asadullah; Din, Irfan Ud; Rehman, Rana Asif; Kim, Byung-Seo

doi:10.32604/cmc.2021.017658

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Vehicle movement at high speeds also causes the topology to change rapidly, adding unnecessary delays. Tariq et al [11] addressed the challenges posed by vehicle movement by proposing a combination of an NDN and an SDN called NDSDoV. NDSDoV uses edge controllers to maintain and manage realtime vehicular topology.…”

Section: Related Workmentioning

confidence: 99%

Research on Task-Offloading Delay in the IoV Based on a Queuing Network

Wei,

Liang

2024

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Research on Task-Offloading Delay in the IoV Based on a Queuing Network

Wei,

Liang

2024

IEEE Access

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“…Compared with the traditional network, an SDN network can provide better service quality for existing fields such as Edge Computing and the Internet of Things [1]. In emerging fields such as the 5G network, Fog Computing [2], and Internet of Vehicles (IoV) [3], the SDN network has broader application prospects, and it can combine with Network Function Virtualization (NFV), Machine Learning [4], Load Balancing [5] and other technologies to generate greater potential value. Therefore, it has extremely high value to research the test work of SDN networks.…”

Section: Introductionmentioning

confidence: 99%

ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

Yao¹,

Wang²,

Weng³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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With the increasing number of switches in Software-Defined Networking (SDN), there are more and more faults rising in the data plane. However, due to the existence of link redundancy and multi-path forwarding mechanisms, these problems cannot be detected in time. The current faulty path detection mechanisms have problems such as the large scale of detection and low efficiency, which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN. Concerning this issue, we propose an efficient network path fault testing model ProbD based on probability detection. This model achieves a high probability of detecting arbitrary path fault in the form of small-scale random sampling. Under a certain path fault rate, ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times. After a small number of experiments, the ProbD model can correctly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network. The final experimental results show that, compared with the full path coverage test, the ProbD model based on probability detection can achieve efficient network testing with less overhead. Besides, the larger the network scale is, the more overhead will be saved.

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