An Efficient and Lightweight Load Forecasting for Proactive Scaling in 5G Mobile Networks

Abstract

The number of connected devices is increasing with the emergence of new services and trends. This phenomenon is leading to a traffic growth over both the control and the data planes of the mobile core network. It is expected that the traffic will increase more and more with the installation of the new generation of mobile networking (5G) as it offers more services that are intended to be connected over the same network in addition to the legacy ones. Therefore the 3GPP group has rethought the architecture of the New Generation Core (NGC) by defining its components as Virtualized Network Functions (VNF). However, scalability techniques should be envisioned in order to answer the needs, in term of resource provisioning, without degrading the Quality Of Service (QoS) already offered by hardware based core networks. Neural networks, and in particular deep learning, having shown their effectiveness in predicting time series, could be good candidates for predicting traffic evolution. In this paper, we proposed a novel solution to generalize neural networks while accelerating the learning process by using K-mean clustering, and a Monte-Carlo method. We benchmarked multiple types of deep neural networks using real operator's data in order to compare their efficiency in predicting the upcoming network load for dynamic and proactive resource provisioning. The proposed solution allow obtaining very good predictions of the traffic evolution while reducing by 50% the time needed for the learning phase.

Clustering Research in Fifth Generation Mobile Communication Networks

“…Now themes of this research will be presented in this section and the deficiency in the literature on this subject will be eliminate. Clustering 32, ( 100% ) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32] Clustering algorithm 18, ( 56% ) [3,5,6,7,8,9,11,12,13,20,21,22,23,24,25,29,31,32] Networks 29, ( 91% ) [1,<...>…”

“…Clustering in telecommunication frameworks, showed up in the system of specially appointed networking, which was proposed as a promising answer for infrastructure denied networks, for example, crisis communication power outages or to convey fit for wireless networks [3,6]. A clustering calculation can parcel sensors into various clusters.…”

Dimensioning V2N Services in 5G Networks Through Forecast-Based Scaling

“…In doing so, scaling decisions can be anticipated, avoiding the increase of the attachment time of user equipment and the percentage of rejected requests. A similar solution is also proposed in [23] targeting a dynamic and proactive resource allocation to the AMF, where LSTM, Convolutional Neural Networks (CNN), and a combined CNN-LSTM are used to forecast the traffic evolution of a mobile network.…”

Mitigando Ataques com a Orquestração de VNFs Baseadas em Contêineres Usando Aprendizado Supervisionado

“…Assim, apresentam um framework conceitual (SecMANO) para gerenciamento de seguranc ¸a baseado em NFV e orquestrac ¸ão de servic ¸os, com o objetivo de implantar e gerenciar de forma dinâmica e adaptativa as func ¸ões de seguranc ¸a de acordo com as demandas dos usuários e clientes. Alawe et al (2018) propõem uma soluc ¸ão para prever a evoluc ¸ão do tráfego na rede com base em redes neurais, buscando escalabilidade para atender às necessidades, como provisionamento de recursos, sem degradar a Qualidade de Servic ¸o (QoS). A soluc ¸ão trata de generalizar redes neurais enquanto acelera o processo de aprendizado usando agrupamento K-means e um método de Monte-Carlo.…”