AI-Driven Management of Dynamic Multi-Tenant Cloud Networks

To find a solution for the increased complexity of managing load and dynamic change of virtual network topology in data centers, this article considers and advocates the use of artificial intelligence (AI) and machine-learning-based approaches. This approach would meet the service requirements for virtual machine (VM) assignments and their communications scenarios. We show an analysis that the time required to reconfigure a virtual network of a tenant is successfully decreased when AI assisted method is used. We compare the effectiveness of multi-tenancy structures of cloud networks both with and without AI option at the load balancer segment of a cloud data center.