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.

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

The upcoming mobile core network, which will be based on Virtual Network Functions (VNF), will face an increase of data traffic on both data and control planes This is due to the increase of the number of connected devices and the newly 5G supported-services like IoT, Connected Health Care etc Therefore dynamic and accurate 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 Although provisioning new resources is easier as it is a matter of software deployment, the strategy to use (when to scale and how much to scale) remains complex In this paper we propose scaling techniques based on neural networks to forecast the upcoming load Hence scheduling the resource provisioning should be in a manner that all the needed resources will be deployed and active when the load increases In the same way, it will scale-in the unneeded resources when the traffic load decreases The proposal is tested via discrete event simulations using a traffic load dataset provided by a Network Operator The results show clearly the robustness of our proposal compared to a threshold-based scaling technique

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Smart Scaling of the 5G Core Network: An RNN-Based Approach

The steps toward all over IP have defined to the IP Multimedia Subsystem (IMS) as the de facto technology for end-to-end multimedia service provisioning in 5G. However, the unpredictable growth of users in 5G requires to improve IMS scalability to handle dynamic user traffic. Several works have addressed this issue by introducing auto-scaling mechanisms in virtualized IMS (vIMS) architectures. However, the current vIMS deployments use monolithic designs that do not allow finer-scalability. In this paper, we present μvIMS, an architecture that uses microservices to provide finer-scalability and more effective resource usage than regular monolithic design. To test our architecture, we evaluate μvIMS prototype regarding CPU usage, RAM usage, Successful Call Rate (SCR), and latency metrics. Our test results reveal that μvIMS achieves a higher SCR, using the available resources effectively with a negligible latency increasing. Thus, we can state that dividing the monolithic vIMS architecture in microservices allows providing finer-scalability.

μvIMS: A Finer-Scalable Architecture Based on Microservices

Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions. 

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Our paper deals with structure of Home Subscriber Server in IP Multimedia Subsystem and mathematical models for optimizing this database. The aim of our work is choose and verify proper models for dimensioning of traffic in the HSS. In this paper we describe the Markov M/M/1/K and M/M/m/K models and Erlang C formula. Simulations are graphically interpreted in MATLAB and results are discussed.

Theoretical Approach to Optimization of Home Subscriber Server in IP Multimedia Subsystem

Todays telecommunication networks are plagued with ever increasing demands of scalability and service agility. Network function virtualization (NFV) potentially addresses the issue of slow scaling and reduced time to introduce new services, which are hindered in network services built up on proprietary, dedicated hardware. In this paper we explore one of the merits of NFV of massive scalability in virtual networks particularly in virtual IP based multimedia subsystem (vIMS). In our practical approach we study the effect of different control parameters used for scaling policy. Moreover, we explore the behavior of the IP based multimedia subsystem deployed on a cloud by varying levels of load and stress.

Tahir Masood

Papers

Load based automatic scaling in virtual IP based multimedia subsystem