Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

Recently,the fifth generation(5G)of mobile networks has been deployed and various ranges of mobile services have been provided.The 5G mobile network supports improved mobile broadband,ultra-low latency and densely deployed massive devices.It allows multiple radio access technologies and interworks them for services.5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies.However,conventional traffic steering techniques do not consider dynamic network conditions efficiently.In this paper,we propose a network aided traffic steering technique in 5G mobile network architecture.5G mobile systems monitor network conditions and learn with network data.Through a machine learning algorithm such as a feed-forward neural network,it recognizes dynamic network conditions and then performs traffic steering.The proposed scheme controls traffic for multiple radio access according to the ratio of measured throughput.Thus,it can be expected to improve traffic steering efficiency.The performance of the proposed traffic steering scheme is evaluated using extensive computer simulations.

Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)devices.The millimeter-Wave(mmWave)communications will become a potential core technology to increase the capacity of Radio Networks(RN)and enable Multiple-Input and Multiple-Output(MIMO)of Radio Remote Head(RRH)technology.However,the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently.The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests.To handle this issue effectively,Machine Learning(ML)algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity conditions.This paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm,namely K-Nearest Neighbor(KNN),to improve the communication Quality of Service(QoS)in real-time IoT networks.KNN supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher power.The experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters,including communication reliability,latency,and throughput.

LTSS： Load-Adaptive Traffic Steering and Forwarding for Security Services in Multi-Tenant Cloud Datacenters

Currently, different kinds of security devices are deployed in the cloud datacenter environment and tenants may choose their desired security services such as firewall and IDS （intrusion detection system）. At the same time, tenants in cloud computing datacenters are dynamic and have different requirements. Therefore, security device deployment in cloud datacenters is very complex and may lead to inefficient resource utilization. In this paper, we study this problem in a software-defined network （SDN） based multi-tenant cloud datacenter environment. We propose a load-adaptive traffic steering and packet forwarding scheme called LTSS to solve the problem. Our scheme combines SDN controller with TagOper plug-in to determine the traffic paths with the minimum load for tenants and allows tenants to get their desired security services in SDN-based datacenter networks. We also build a prototype system for LTSS to verify its functionality and evaluate performance of our design.