An efficient resource allocation strategy for multilayer networks

2021 ◽  
Vol 35 (05) ◽  
pp. 2150073
Author(s):  
Yongqiang Zhang ◽  
Yaming Li ◽  
Min Li ◽  
Jinlong Ma ◽  
Zhaohui Qi

The resource allocation strategy plays an important role in the improvement of network traffic capacity. In order to solve the problem of network congestion, an efficient resource allocation strategy is proposed for multilayer networks to optimize the utilization efficiency of network resources. With the proposed strategy, the network resources are allocated to nodes more reasonable and the network congestions are evidently reduced. Simulation experiments show that the proposed resource allocation strategy can greatly improve the traffic capacity of the multilayer networks compared with the average resource allocation strategy. The proposed strategy can give full play to the traffic resources of multilayer networks, which has guiding significance for optimizing the existing networks and building new networks.

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 199829-199839
Author(s):  
Anees Ur Rehman ◽  
Zulfiqar Ahmad ◽  
Ali Imran Jehangiri ◽  
Mohammed Alaa Ala'Anzy ◽  
Mohamed Othman ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6542
Author(s):  
Ida Nurcahyani ◽  
Jeong Woo Lee

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.


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