Machine Learning Methods for Slice Admission in 5G Networks

The paper discusses how the slice admission problem can be aided by machine learning strategies. Results show that both supervised and reinforcement learning might lead to profit maximization while containing losses due to performance degradation

Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture

The open radio access network (O-RAN) architecture's native virtualization and embedded intelligence facilitate RAN slicing and enable comprehensive end-to-end services in post-5G networks. However, any vulnerabilities could harm security. Therefore, artificial intelligence (AI) and machine learning (ML) security threats can even threaten O-RAN benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice while addressing secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture. We solve this problem on two-time scales using mathematical methods for determining the predefined number of VNFs on a large time scale and the proximal policy optimization (PPO), a Deep Reinforcement Learning algorithm, for solving dynamic service admission control and power minimization for different slices on a small-time scale. To secure the ML system for O-RAN, we implement a moving target defense (MTD) strategy to prevent poisoning attacks by adding uncertainty to the system. Our experimental results show that the proposed PPO-based service admission control approach achieves an admission rate above 80\% and that the MTD strategy effectively strengthens the robustness of the PPO method against adversarial attacks.Comment: 6 page

A Resource Sharing Method for Reliable Slice as a Service Provisioning in 5G Metro Networks

This paper proposes a dynamic slice provisioning analysis in a 5G metro network with reliability guarantees and possible sharing of backup resources. Performance of dedicated (DP) and shared (SP) protection solutions are evaluated with respect to slice resource allocation (i.e., bandwidth and processing units). The main results show a remarkable saving, in terms of slice acceptance rate, by applying SP solutions with respect to conventional DP ones

AI gym for Networks

5G Networks are delivering better services and connecting more devices, but at the same time are becoming more complex. Problems like resource management and control optimization are increasingly dynamic and difficult to model making it very hard to use traditional model-based optimization techniques. Artificial Intelligence (AI) explores techniques such as Deep Reinforcement Learning (DRL), which uses the interaction between the agent and the environment to learn what action to take to obtain the best possible result. Researchers usually need to create and develop a simulation environment for their scenario of interest to be able to experiment with DRL algorithms. This takes a large amount of time from the research process, while the lack of a common environment makes it difficult to compare algorithms. The proposed solution aims to fill this gap by creating a tool that facilitates the setting up of DRL training environments for network scenarios. The developed tool uses three open source software, the Containernet to simulate the connections between devices, the Ryu Controller as the Software Defined Network Controller, and OpenAI Gym which is responsible for setting up the communication between the environment and the DRL agent. With the project developed during the thesis, the users will be capable of creating more scenarios in a short period, opening space to set up different environments, solving various problems as well as providing a common environment where other Agents can be compared. The developed software is used to compare the performance of several DRL agents in two different network control problems: routing and network slice admission control. A novel DRL based solution is used in the case of network slice admission control that jointly optimizes the admission and the placement of traffic of a network slice in the physical resources.As redes 5G oferecem melhores serviços e conectam mais dispositivos, fazendo com que se tornem mais complexas e difíceis de gerir. Problemas como a gestão de recursos e a otimização de controlo são cada vez mais dinâmicos e difíceis de modelar, o que torna difícil usar soluções de optimização basea- das em modelos tradicionais. A Inteligência Artificial (IA) explora técnicas como Deep Reinforcement Learning que utiliza a interação entre o agente e o ambiente para aprender qual a ação a ter para obter o melhor resultado possível. Normalmente, os investigadores precisam de criar e desenvolver um ambiente de simulação para poder estudar os algoritmos DRL e a sua interação com o cenário de interesse. A criação de ambientes a partir do zero retira tempo indispensável para a pesquisa em si, e a falta de ambientes de treino comuns torna difícil a comparação dos algoritmos. A solução proposta foca-se em preencher esta lacuna criando uma ferramenta que facilite a configuração de ambientes de treino DRL para cenários de rede. A ferramenta desenvolvida utiliza três softwares open source, o Containernet para simular as conexões entre os dispositivos, o Ryu Controller como Software Defined Network Controller e o OpenAI Gym que é responsável por configurar a comunicação entre o ambiente e o agente DRL. Através do projeto desenvolvido, os utilizadores serão capazes de criar mais cenários em um curto período, abrindo espaço para configurar diferentes ambientes e resolver diferentes problemas, bem como fornecer um ambiente comum onde diferentes Agentes podem ser comparados. O software desenvolvido foi usado para comparar o desempenho de vários agentes DRL em dois problemas diferentes de controlo de rede, nomeadamente, roteamento e controlo de admissão de slices na rede. Uma solução baseada em DRL é usada no caso do controlo de admissão de slices na rede que otimiza conjuntamente a admissão e a colocação de tráfego de uma slice na rede nos recursos físicos da mesma

A machine learning approach to 5G infrastructure market optimization

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent "slices" of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Laboratories Europe was supported by the 5G-Transformer project (Grant Agreement No. 761536)

Network Slicing Automation: Challenges and Benefits

Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs).\ua0 In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span.\ua0Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems