Network packet

Multi-core processor

Engineering

Transport engineering

Network traffic simulation

Network traffic control

Operating system

Telecommunications

Scalability

Core (optical fiber)

Computer science

Load balancing (electrical power)

Grid

Distributed computing

Computer network

Traffic generation model

Mathematics

Cellular network

Artificial intelligence

Geometry

Artificial neural network

Floating car data

Traffic congestion

Core network

Software deployment

Machine learning

IEEE Network

5G is expected to provide network connectivity to not only classical devices (i.e., tablets, smartphones, etc.) but also to the IoT, which will drastically increase the traffic load carried over the network. 5G will mainly rely on NFV and SDN to build flexible and on-demand instances of functional networking entities via VNFs. Indeed, 3GPP is devising a new architecture for the core network, which replaces point-to-point interfaces used in 3G and 4G by producer/consumer-based communication among 5G core network functions, facilitating deployment over a virtual infrastructure. One big advantage of using VNFs is the possibility of dynamic scaling, depending on traffic load (i.e., instantiate new resources to VNFs when the traffic load increases and reduce the number of resources when the traffic load decreases). In this article, we propose a novel mechanism to scale 5G core network resources by anticipating traffic load changes through forecasting via ML techniques. The traffic load forecast is achieved by using and training a neural network on a real dataset of traffic arrival in a mobile network. Two techniques were used and compared: RNN, more specifically LSTM; and DNN. Simulation results show that the forecast-based scalability mechanism outperforms the threshold-based solutions, in terms of latency to react to traffic change, and delay to have new resources ready to be used by the VNF to react to traffic increase.

Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach