The University of Edinburgh

Chaoyun Zhang

Paul Patras

Marco Fiore

To be able to adequately provide desired services over a 5G mobile service network, the 5G communication infrastructures requires a much-improved flexibility in resource management. Network operators are foreseen to deploy network slicing, by isolating dedicated resources and providing customised logical instances of the physical infrastructure to each service. A critical operation in performing management and orchestration of network resources is the anticipatory provisioning of isolated capacity to each network slice. Accordingly, it is necessary to obtain an estimate of service level demands. However, the estimation of such service level demands is typically obtained via deep packet inspection, which is a resource intensive and time-consuming process. Therefore, it is typically not possible to provide updated accurate estimates at a frequency suitable for use in accurate prediction of a future per-service traffic consumption, without an undesirable level of computational and time resources being required. The present invention provides a distributed network traffic data decomposition method which makes use of a neural network to provide an accurate future per-service traffic consumption prediction without deep-packet inspection or another resource intensive analysis method.

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Edinburgh, United Kingdom

Chaoyun Zhang