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End-to-End 5G Service Deployment and Orchestration in Optical Networks with QoE Guarantees
... There are three types of connected 5G services: eMBB (Enhanced Mobile Broadband), URLLC (Ultra Reliable Low Latency Communications) et mMTC (massive Machine Type Communications). They are classified as enhanced mobile services [3].) ...
... Example of using mMTC: detection, counting and monitoring devices.... To summarize this section, we can say that the 5G will bring a considerable improvement on three key points of the telecommunication: the latency, the flow and the density. eMBB aims to meet the demand of the population for an increasingly digital lifestyle and Comparison between different 5G architecture for a better The International Conferences on Smart City Application, Integration of these services and proposal of an improved October 2019, Casablanca MOROCCO architecture 3 focuses on high bandwidth services, such as high definition (HD) video, virtual reality (VR) and augmented reality (RA). uRLLC aims to meet the expectations of the demanding digital sector and focuses on latency-sensitive services, such as remote management. ...
The purpose of 5G wireless technology is to support three generic services, which are: enhanced mobile broadband (eMBB), massive machine-based communications (mMTC), and ultrareliable low latency communications (URLLC). The heterogeneity of services can be compensated by slicing the network, which allocates resources to each service to provide performance guarantees and be isolated from other services.
Several architectures have been proposed by several research units for a better integration of these services. This paper begins with an overview of the connected services managed by the 5G. then we also discuss existing architectures. Finally, we will try to propose our improved architecture.
... There are related research initiatives undertaken to address the problem of network slicing in 5G and next generation SDN. For instance, Montero et al. [4] investigated and proposed an architectural scheme that can support end-to-end monitoring and provisioning of different services for 5G verticals over optical networks. The scheme employs an end-to-end optical orchestrator to manage network slice deployment while guaranteeing the defined Quality of Experience (QoE) levels. ...
... Three provisioning schemes have been proposed in [59] by constructing a virtual auxiliary graph that decomposes the physical infrastructure into several layered graphs, according to the spectrum slot requirements of a virtual optical network request. Network services deployment and orchestration for network slice in inter-ODCNs have been developed in [60]. OpenStack-based orchestrator deploys the VMs for IT requirements by the path computation engine and contacts with the OpenDaylight SDN controller to guarantee the network configuration. ...
The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.
Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model.
Commonly, the network configuration leans upon the operators’ experience to operate network, including command-line configuration, middle-ware scripts, and troubleshooting. However, with the rise of neoteric B5G services, the manual way lacks flexibility and timeliness, resulting in an unsatisfactory level of configuration. It is necessary to consider a manual free configuration way for transport network. To cope with this problem, we present an intent-driven network architecture with self-adapting slicing policy and slices reconfiguration in an intent-orient manner. Aiming at intent request, intent analysis based on latent dirichlet allocation is introduced to establish the semantic graph to comprehend and enact the required slicing configuration language, namely intent translation. Then, in line with intent translation, we propose a self-adapted slicing policy generation and optimization base on deep reinforcement learning (SPG-RL) to find combined strategies that meet the intent requirements by dynamically integrating fine-grained slicing policies. Finally, deep neural evolution network (DNEN)-assisted model (SPG-RL-DNEN) is introduced to locate the incompatible slices at the millisecond level for slicing reconfiguration. When the network entropy reaches the threshold, SPG-RL-DNEN would reconfigure the incompatible slices for intent guarantee. The efficiency of our proposal are verified on enhanced SDN testbed.
Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems.
