A Cloud Native Solution for Dynamic Auto Scaling of MME in LTE

Due to rapid growth in the use of mobile devices and as a vital carrier of IoT traffic, mobile networks need to undergo infrastructure wide revisions to meet explosive traffic demand. In addition to data traffic, there has been a significant rise in the control signaling overhead due to dense deployment of small cells and IoT devices. Adoption of technologies like cloud computing, Software Defined Networking (SDN) and Network Functions Virtualization (NFV) is impressively successful in mitigating the existing challenges and driving the path towards 5G evolution. However, issues pertaining to scalability, ease of use, service resiliency, and high availability need considerable study for successful roll out of production grade 5G solutions in cloud. In this work, we propose a scalable Cloud Native Solution for Mobility Management Entity (CNS-MME) of mobile core in a production data center based on micro service architecture. The micro services are lightweight MME functionalities, in contrast to monolithic MME in Long Term Evolution (LTE). The proposed architecture is highly available and supports auto-scaling to dynamically scale-up and scale-down required micro services for load balancing. The performance of proposed CNS-MME architecture is evaluated against monolithic MME in terms of scalability, auto scaling of the service, resource utilization of MME, and efficient load balancing features. We observed that, compared to monolithic MME architecture, CNS-MME provides 7% higher MME throughput and also reduces the processing resource consumption by 26%

Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto- Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques). The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations.This research work is partially supported by the European Union’s Horizon 2020 research and innovation program under the 5G!Pagoda project, the MATILDA project and the Academy of Finland 6Genesis project with grant agreement No. 723172, No. 761898 and No. 318927, respectively. It was also partially funded by the Academy of Finland Project CSN - under Grant Agreement 311654 and the Spanish Ministry of Education, Culture and Sport (FPU Grant 13/04833), and the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (TEC2016-76795-C6- 4-R)

An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC

The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy

Infrastructure sharing of 5G mobile core networks on an SDN/NFV platform

When looking towards the deployment of 5G network architectures, mobile network operators will continue to face many challenges. The number of customers is approaching maximum market penetration, the number of devices per customer is increasing, and the number of non-human operated devices estimated to approach towards the tens of billions, network operators have a formidable task ahead of them. The proliferation of cloud computing techniques has created a multitude of applications for network services deployments, and at the forefront is the adoption of Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV). Mobile network operators (MNO) have the opportunity to leverage these technologies so that they can enable the delivery of traditional networking functionality in cloud environments. The benefit of this is reductions seen in the capital and operational expenditures of network infrastructure. When going for NFV, how a Virtualised Network Function (VNF) is designed, implemented, and placed over physical infrastructure can play a vital role on the performance metrics achieved by the network function. Not paying careful attention to this aspect could lead to the drastically reduced performance of network functions thus defeating the purpose of going for virtualisation solutions. The success of mobile network operators in the 5G arena will depend heavily on their ability to shift from their old operational models and embrace new technologies, design principles and innovation in both the business and technical aspects of the environment. The primary goal of this thesis is to design, implement and evaluate the viability of data centre and cloud network infrastructure sharing use case. More specifically, the core question addressed by this thesis is how virtualisation of network functions in a shared infrastructure environment can be achieved without adverse performance degradation. 5G should be operational with high penetration beyond the year 2020 with data traffic rates increasing exponentially and the number of connected devices expected to surpass tens of billions. Requirements for 5G mobile networks include higher flexibility, scalability, cost effectiveness and energy efficiency. Towards these goals, Software Defined Networking (SDN) and Network Functions Virtualisation have been adopted in recent proposals for future mobile networks architectures because they are considered critical technologies for 5G. A Shared Infrastructure Management Framework was designed and implemented for this purpose. This framework was further enhanced for performance optimisation of network functions and underlying physical infrastructure. The objective achieved was the identification of requirements for the design and development of an experimental testbed for future 5G mobile networks. This testbed deploys high performance virtualised network functions (VNFs) while catering for the infrastructure sharing use case of multiple network operators. The management and orchestration of the VNFs allow for automation, scalability, fault recovery, and security to be evaluated. The testbed developed is readily re-creatable and based on open-source software

Operational Aspects of Network Slicing in LTE and 5G Architectures

With the advances in cloud computing, the telecom operators are moving towards virtualization and cloud model. The future mobile networks are going to be deployed in the cloud with the help of Software Defined Networks (SDN) and Network Functions Virtualization (NFV) technologies. This thesis discusses various aspects related to prototyping and orchestration of slicing in LTE and 5G core networks using open-source technologies. The network slices are created in the user plane of the core network for differentiating the user traffic in terms of their QoS requirements. The Service Based Architecture (SBA) of 5G core is realized with the help of Google Remote Procedure Call (gRPC) as Service Based Interface (SBI) and Consul as Network Function Repository Function. On top of the setup created, the concept of Look-aside Load Balancing is used, to suit the 5G SBA and efficiently handle the incoming traffic load. With the Access and Mobility Management Function as a use case for load balancing, it is concluded that carefully chosen load balancing algorithms can significantly lessen the control plane latency when compared to simple random or round-robin schemes

Leveraging Cloud-based NFV and SDN Platform Towards Quality-Driven Next-Generation Mobile Networks

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand for network services and serve as an enabler for key emerging technologies such as the Internet of Things (IoT) and 5G networking. The thesis considers different approaches and platforms to serve as an NFV/SDN based cloud applications while closely considering how such an environment deploys its virtualized services to optimize the network and reducing their costs. The thesis starts first by defining the standards of adopting microservices as architecture for NFV. Then, it focuses on the latency-aware deployment approach of virtual network functions (VNFs) forming service function chains (SFC) in a cloud environment. This approach ensures that NSPs still meet their strict quality of service and service level agreements while considering both functional and non-functional constraints of the NFV-based applications such as, delay, resource allocation, and intercorrelation between VNF instances. In addition, the thesis proposes a detailed approach on recovering and handling of those instances by optimizing the decision of migrating or re-instantiating the virtualized services upon a sudden event (failure/overload…). All the proposed approaches contribute to the orchestration of NFV applications to meet the requirements of the IoT and NGNs era

AI-Based Traffic Forecasting in 5G Network

Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. Since a significant number of network slices are deployed over a 5G network, it is crucial to evaluate the resource requirements of each network slice and how they evolve over time. Mobile network carriers should investigate strategies for network optimization and resource allocation due to the steadily increasing mobile traffic. Network management and optimization strategies will be improved if mobile operators know the cellular traffic demand at a specific time and location beforehand. The most effective techniques nowadays devote computing resources in a dynamic manner based on mobile traffic prediction by machine learning techniques. However, the accuracy of the predictive models is critically important. In this work, we concentrate on forecasting the cellular traffic for the following 24 hours by employing temporal and spatiotemporal techniques, with the goal of improving the efficiency and accuracy of mobile traffic prediction. In fact, a set of real-world mobile traffic data is used to assess the efficacy of multiple neural network models in predicting cellular traffic in this study. The fully connected sequential network (FCSN), one-dimensional convolutional neural network (1D-CNN), single-shot learning LSTM (SS-LSTM), and autoregressive LSTM (AR-LSTM) are proposed in the temporal analysis. A 2-dimensional convolutional LSTM (2D-ConvLSTM) model is also proposed in the spatiotemporal framework to forecast cellular traffic over the next 24 hours. The 2D-ConvLSTM model, which can capture spatial relations via convolution operations and temporal dynamics through the LSTM network, is used after creating geographic grids. The results reveal that FCSN and 1D-CNN have comparable performance in univariate temporal analysis. However, 1D-CNN is a smaller network with less number of parameters. One of the other benefits of the proposed 1D-CNN is having less complexity and execution time for predicting traffic. Also, 2D-ConvLSTM outperforms temporal models. The 2D-ConvLSTM model can predict the next 24-hour traffic of internet, sms, and call with root mean square error (RMSE) values of 75.73, 26.60, and 15.02 and mean absolute error (MAE) values of 52.73, 14.42, and 8.98, respectively, which shows better performance compared to the state of the art methods due to capturing variables dependencies. It can be argued that this network has the capability to be utilized in network management and resource allocation in practical applications