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Artificial Intelligence for network function autoscaling in a cloud-native 5G network
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DOI：10.1016/j.compeleceng.2022.108327.png)
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Abstract
En 中文
5G Core Network (CN) has been designed for operating in a cloud-native manner, dynamically managed and micro-services oriented. This has been widely accepted in industry as the essential solution for realizing 5G networks, due to its potential to deliver scalability and dynamic configurability. In the case of 5G networks, orchestrators need to handle Virtual Network Functions which have different requirements when compared to cloud-based services and need to dynamically adapt/reconfigure based on the demand. In this work, we detail the potential challenges when deploying a cloud-native 5G CN, and develop schemes for dynamic scaling of network functions using Artificial Intelligence (AI). The need for a customized orchestrator deployment is demonstrated in a real test-bed setup, with the case of the Access and Mobility Function. Moreover, a Deep Learning AI approach is applied for proactively scaling network functions, providing two major benefits: A significantly higher number of users can be admitted to the 5G Network and traffic is balanced evenly among the different AMF replicas.
Keywords:
5G
Beyond5G
Autoscaling
Artificial Intelligence
AI
Cloud
Journal
C
IF:
4.9
Papers:
6.7K
Citations:
1.3W
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