Concept Of Network Slicing Research Articles

Transport SDN architecture for multi-layer transport slicing

The proposed architecture advances the concept of network slicing, crucial for beyond 5G services, by enabling dynamic resource allocation and customized partitioning in managed network infrastructures. This architecture addresses the challenges of provisioning end-to-end (E2E) slices across diverse network domains, which is complicated by technological heterogeneity and the variety of vendor solutions. By introducing a standardized transport network solution, we ensure seamless integration, equitable treatment of service requests, and the ability to meet diverse demands. The architecture is centered around a multi-layer transport network slicing architecture, which allows for the division of transport networks into virtual autonomous segments, each tailored for specific services or applications. This segmentation is essential for providing differentiated and personalized 5G services, optimizing network performance, and maximizing resource application. A key component of this architecture is the transport slice controller (TSC), which controls the provision and life-cycle management of transport slices, ensuring a standardized approach in the industry for the definition and realization of slices.

Network Type Recognition Using Machine Learning Techniques

The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation of applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and an immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision-making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc). Index Terms: Supervised Learning, Feature Engineering, Python, Network Slicing, Telecom, Classification Problems, EDA – Exploratory Data Analysis.

DRL-Based Slice Admission Using Overbooking in 5G Networks

In 5G-and-beyond networks, the concept of Network Slicing is used to support multiple independent and co-existing logical networks on a physical network infrastructure. The infrastructure provider (InP) owns the set of virtual and physical resources that are used to support the tenant slice requests. Each slice request specifies a service level agreement (SLA) that contains the required slice-level resources (computation and communication) and the revenue provided by the tenant. Due to limited resources, the InP cannot accommodate all requests made by the tenants. In general, it has been found that tenants tend to overestimate their resource demands (e.g., for 5G Core computation) to reduce possible SLA violations. In this paper, we consider two major slice types: Elastic (low priority and low revenue) and Inelastic (high priority and high revenue). We apply the concept of overbooking, where the InP accepts more slices while considering slice priorities in order to maximize the overall revenue and utilization. We consider a multi-tenant environment and propose a slice admission system named PRLOV, which is based on Reinforcement Learning (RL) and prediction methods. The system predicts the future resource demands of Elastic slices and applies an opportunistic overbooking technique to overbook InP for accepting more slices. In addition, the admission decision is formulated as an Markov Decision Process (MDP) problem and solved using standard RL techniques (Policy Iteration, Q-Learning, DQN). The performance of our proposed work is compared against three other heuristics (Basic, Prediction, PRL) that do not use overbooking. Data traces from the Materna data center networks were used for prediction purposes. The important performance metrics measures include InP total revenue, the acceptance rate of respective slices and overall resource utilization for different slices. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization. Simulation results show that PRLOV provides a revenue gain of 6%, 26%, and 102% compared to the PRL, Prediction and Basic scheme.

Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments.

Along with the adoption of 5G, the development of neutral host solutions provides a unique opportunity for mobile networks operators to accommodate the needs of emerging use-cases and in the consolidation of new business models. By exploiting the concept of network slicing, as one key enabler in the transition to 5G, infrastructure and service providers can logically split a shared physical network into multiple isolated and customized networks to flexibly address the specific demands of those tenant slices. Motivated by this reality, the H2020 5GCity project proposed a novel 5G-enabled neutral host framework for three European cities: Barcelona (ESP), Bristol (UK), and Lucca (IT). This article revises the main achievements and contributions of the 5GCity project, focusing on the deployment and validation of the proposed framework. The developed neutral host framework encompasses two main parts: the infrastructure and the software platform. A detailed description of the framework implementation, in terms of functional capabilities and practical implications of city-wide deployments, is provided in this article. This work also presents the performance evaluation of the proposed solution during the implementation of real vertical use cases. Obtained results validate the feasibility of the neutral host model and the proposed framework to be deployed in city-wide 5G infrastructures.

A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture

The 5G communication standard is characterized by an increased softwarization, allowing a higher flexibility able to cope with different requirements and services. In particular, Network Function Virtualization (NFV) is a recently introduced technology that enables a software implementation of different network functions exploiting virtualization techniques, hence, enabling their flexible deployment upon system requirements. Boosted by NFV, the concept of network slicing is gaining great attention in 5G networks. The idea is that physical communication and computing resources are sliced in multiple end-to-end logical networks, each one tailored to best support a specific service. The advantages of NFV, in the network slicing context, are even more evident in distributed computing environments, such as the edge-to-cloud continuum, recently introduced for enabling a flexible deployment of multiple functions. In particular, thanks to the introduction of cloud-native technologies, based on the usage of containerization and microservice technologies, the virtual network functions (VNFs) deployment and their orchestration is an easy operation, allowing the on-the-fly network configuration. Gaining from the NFV, Network Slicing and Edge-to-Cloud continuum paradigms, we propose a new network function allocation problem for multi-service 5G networks, able to deploy network functions on a distributed computing environment depending on the service requests. The proposed approach jointly considers Radio Access Network (RAN) and Core Network (CN) functions and, differently from other approaches, introduces an option able to bias the function placement depending on the service requirements, allowing a fast-and-easy operator-side deployment of the network functions. We propose to solve the problem through a Genetic Algorithm able to approach the optimal solution but with reduced complexity and execution time. The performance is compared with two other heuristic algorithms and with an exhaustive search algorithm, introduced as benchmarks, showing the benefits of the selected solution in terms of performance, flexibility and complexity.

Model-based reinforcement learning for router port queue configurations

Fifth-generation (5G) systems have brought about new challenges toward ensuring Quality of Service (QoS) in differentiated services. This includes low latency applications, scalable machine-to-machine communication, and enhanced mobile broadband connectivity. In order to satisfy these requirements, the concept of network slicing has been introduced to generate slices of the network with specific characteristics. In order to meet the requirements of network slices, routers and switches must be effectively configured to provide priority queue provisioning, resource contention management and adaptation. Configuring routers from vendors, such as Ericsson, Cisco, and Juniper, have traditionally been an expert-driven process with static rules for individual flows, which are prone to sub optimal configurations with varying traffic conditions. In this paper, we model the internal ingress and egress queues within routers via a queuing model. The effects of changing queue configuration with respect to priority, weights, flow limits, and packet drops are studied in detail. This is used to train a model-based Reinforcement Learning (RL) algorithm to generate optimal policies for flow prioritization, fairness, and congestion control. The efficacy of the RL policy output is demonstrated over scenarios involving ingress queue traffic policing, egress queue traffic shaping, and one-hop router coordinated traffic conditioning. This is evaluated over a real application use case, wherein a statically configured router proved sub optimal toward desired QoS requirements. Such automated configuration of routers and switches will be critical for multiple 5G deployments with varying flow requirements and traffic patterns.

Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.

On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds

With the advent of 5G verticals and the Internet of Things paradigm, Edge Computing has emerged as the most dominant service delivery architecture, placing augmented computing resources in the proximity of end users. The resource orchestration of edge clouds relies on the concept of network slicing, which provides logically isolated computing and network resources. However, though there is significant progress on the automation of the resource orchestration within a single cloud or edge cloud datacenter, the orchestration of multi-domain infrastructure or multi-administrative domain is still an open challenge. Towards exploiting the network service marketplace at its full capacity, while being aligned with ETSI Network Function Virtualization architecture, this article proposes a novel Blockchain-based service orchestrator that leverages the automation capabilities of smart contracts to establish cross-service communication between network slices of different tenants. In particular, we introduce a multi-tier architecture of a Blockchain-based network marketplace, and design the lifecycle of the cross-service orchestration. For the evaluation of the proposed approach, we set up cross-service communication in an edge cloud and we demonstrate that the orchestration overhead is less than other cross-service solutions.

A 5G-Based eHealth Monitoring and Emergency Response System: Experience and Lessons Learned

5G is being deployed in major cities across the globe. Although the benefits brought by the new 5G air interface will be numerous, 5G is more than just an evolution of radio technology. New concepts, such as the application of network softwarization and programmability paradigms to the overall network design, the reduced latency promised by edge computing, or the concept of network slicing – just to cite some of them – will open the door to new vertical-specific services, even capable of saving more lives. This article discusses the implementation and validation of an eHealth service specially tailored for the Emergency Services of the Madrid Municipality. This new vertical application makes use of the novel characteristics of 5G, enabling dynamic instantiation of services at the edge, a federation of domains and execution of real on-the-field augmented reality. The article provides an explanation of the design of the use case and its real-life implementation and demonstration in collaboration with the Madrid emergency response team. The major outcome of this work is a real-life proof-of-concept of this system, which can reduce the time required to respond to an emergency in minutes and perform more efficient triage, increasing the chances of saving lives.

RESEARCH OF CYBER SECURITY MECHANISMS IN MODERN 5G CELLULAR NETWORKS

The main feature of the 5G network is Network slicing. This concept enables network resource efficiency, deployment flexibility, and support for rapid growth in over the top (OTT) applications and services. Network Slicing involves splitting the 5G physical architecture into multiple virtual networks or layers. Each network layer (slice) includes control layer functions, user traffic level functions, and a radio access network. Slice isolation is an important requirement that allows the basic concept of Network slicing to be applied to the simultaneous coexistence of multiple fragments in a single infrastructure. This property is achieved by the fact that the performance of each slice should not affect the performance of the other. The architecture of network fragments expands in two main aspects: slice protection (cyber attacks or malfunctions affect only the target slice and have a limited impact on the life cycle of other existing ones) and slice privacy (private information about each slice, such as user statistics) does not exchange between other slices). In 5G, the interaction of the user's equipment with the data networks is established using PDU sessions. Multiple PDU sessions can be active at the same time to connect to different networks. In this case, different sessions can be created using different network functions following the concept of Network Slicing. The concept of "network architecture", which is developed on hardware solutions, is losing its relevance. It will be more appropriate to call 5G a system, or a platform because it is implemented using software solutions. 5G functions are implemented in VNF virtual software functions running in the network virtualization infrastructure, which, in turn, is implemented in the physical infrastructure of data centers, based on standard commercial COTS equipment, which includes only three types of standard devices - server, switch and a storage system. For the correct operation of a network, it is necessary to provide constant monitoring of parameters which are described above. Monitoring is a specially organized, periodic observation of the state of objects, phenomena, processes for their assessment, control, or forecasting. The monitoring system collects and processes information that can be used to improve the work process, as well as to inform about the presence of deviations. There is a lot of network monitoring software available today, but given that 5G is implemented on virtual elements, it is advisable to use the System Center Operations Manager component to monitor network settings and performance and to resolve deviations on time. The Operations Manager reports which objects are out of order sends alerts when problems are detected and provides information to help determine the cause of the problem and possible solutions. So, for the 5G network, it is extremely important to constantly monitor its parameters for the timely elimination of deviations, as it can impair the performance and interaction of smart devices, as well as the quality of communication and services provided. System Center Operations Manager provides many opportunities for this. The purpose and objectives of the work. The work aims to analyze the main mechanisms of cybersecurity in 5G cellular networks

Latency and energy-aware provisioning of network slices in cloud networks

Modern network services are constantly increasing their requirements in terms of bandwidth, latency and cost efficiency. To satisfy these requirements, the concept of network slicing has been introduced in the context of next-generation 5G networks. However, to successfully provision resources to slices, a complex optimization problem must be addressed to allocate resources over a cloud network, i.e., a distributed computing infrastructure interconnected through high-capacity network links. In this study, we propose two new latency and energy-aware optimization models for provisioning 5G slices in cloud networks comprising both distributed computing and network resources. The proposed approaches differ from other existing solutions since we conduct our studies with respect to the end-to-end latency. Relevant models of latency and energy consumption are proposed based on a comprehensive review of the state-of-the-art. To effectively solve those optimization problems, a configurable heuristic is also proposed and investigated over different network topologies. Performance of the proposed heuristic is compared against near-optimal solutions. Moreover, we assess the importance of matching between resource provisioning algorithms and architectural assumptions related to 5G network slices and a proper problem modeling.

Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks

For accommodating the heterogeneous services that are anticipated for the fifth-generation (5G) mobile networks, the concept of network slicing serves as a key technology. Spanning both the core network (CN) and radio access network (RAN), slices are end-to-end virtual networks that share the resources of a physical network. Slicing the RAN can be more challenging than slicing the CN since RAN slicing deals with the distribution of radio resources, which have fluctuating capacity and are harder to extend. Improving multiplexing gains, while assuring the slice isolation is the main challenging task for RAN slicing. This paper provides a flexible and configurable framework for RAN slicing, where diverse requirements of slices are simultaneously taken into account, and slice management algorithms adjust the control parameters of different radio resource management (RRM) mechanisms to satisfy the slices’ service level agreements (SLAs). One of the proposed algorithms is based merely on heuristics and the other one utilizes an artificial neural network (ANN) to predict the behavior of the cellular network and make better decisions in the adjustment of the RRM mechanisms. Furthermore, a protection mechanism is devised to prevent the slices from negatively influencing each other’s performances. A simulation-based analysis demonstrates that in presence of local or global overload of one of the slices, the ANN-based method increases the number of key performance indicators (KPIs) that fulfill their defined SLA targets. Finally, we show that the proposed protection mechanism can force the negative effects of an overloading slice to be contained to that slice and the other slices are not affected as severely.

Network slicing - enabled RAN management for 5G: Cross layer control based on SDN and SDR

Having already entered the initial phase of the 5th generation networks (5G) era, the network ecosystems have already begun comprising a plethora of coexisting 3GPP and non-3GGP Radio Access Technologies (RATs). Deployment scenarios envision a multi-layer use of macro, micro and femto-cells, where multi-mode end devices, supporting different applications, are served by heterogeneous access technologies. At the same time, this heterogeneous environment results in an abstract pool of resources, moving away from the traditional cell concept and exposing the network administrator with the collective resources in time, frequency, and space. An imperative need is thus created, to obtain an overview of the network and radio conditions in order to respond in an optimal way. The concepts of Software Defined Networking (SDN) and Software Defined Radio (SDR), when applied in a coordinated and sophisticated manner, may prove of utmost importance towards addressing the aforementioned challenges. The concept of network slicing -that enables the creation of several virtual networks with diverse characteristics, on top of common infrastructure- further reinforces the flexibility and dynamicity of the network. To this end, we present and evaluate a Cross Layer Controller (CLC), which acts on top of well-established SDN and SDR technologies, and monitors in real-time the conditions in the radio environment, as well as the transport network traffic, in a unified way; based on this dynamic input, CLC enforces sophisticated, end-to-end resource allocation policies and pairing mechanisms between network slices from different network segments, highlighting thus, the feature of cross layer resource management and dynamic adaptation. The experimentation results demonstrate the considerable gains acquired from the proposed solution.

FASTSCALE: A fast and scalable evolutionary algorithm for the joint placement and chaining of virtualized services

Service function chaining or in-line services in network function virtualization is a promising approach for network and service providers as it allows them to dynamically instantiate network functions and interconnect them according to a predetermined policy on-the-fly. It brings flexibility, easy management and rapid deployment of new virtual network services. With the advent of 5G and the concept of network slicing, virtual network services are becoming increasingly complex, not only in terms of topologies, but also in terms of stringent requirements that need to be fulfilled. An optimal deployment of these service function chains, and virtual network services in general, calls for an approach that considers the operational, traffic, and energy costs and QoS constraints jointly from a multi-objective mathematical perspective.In this paper, we propose a multi-objective integer linear program for the joint placement and chaining of virtual network services of different topologies (linear and non-linear) to solve the problem optimally and an evolutionary algorithm (cultural algorithm) for medium and large-scale instances (not limited to 3 to 5 VNFs per SFC). To the best of our knowledge, we are the first to consider this problem from this perspective and propose a solution to it. Extensive experiments demonstrate the effectiveness and efficiency of our proposed evolutionary algorithm versus the optimal solution.

An Overview of Network Slicing for 5G

Besides conventional mobile broadband communication service, 5G is envisioned to support various new use cases from vertical industries. These new scenarios bring diverse and challenging requirements, such as a broader range of performance, cost, security protection, and mobility management. The one-size-fits-all design philosophy applied in existing networks is not viable any more. Slicing a single physical network into several logical networks customized to different unique requirements has emerged as a promising approach to fulfill such divergent requirements in a sustainable way. In this article, we provide a comprehensive survey of 5G network slicing. We first present the driving forces and the concept of network slicing. Then related key enabling technologies, including network function virtualization and modularization, dynamic service chaining, management and orchestration are discussed. The latest progress in 3GPP standardization and industry implementation on 5G network slicing is presented. Finally, the article identifies several important open issues and challenges to inspire further study toward a practical network slicing enabled 5G system.

METHOD FOR FORMING OF MULTISERVICE CORE MOBILE COMMUNICATION NETWORK SLICES

Context. With the growth in the volume of mobile data traffic and the number of services provided by the mobile network, service load flows are also increasing, which necessitates changing the principles, models and methods of telecommunication flows serving in order to ensure the given quality of providing a wide range of services in flexible and cost-effective way. Advantages of scalability and elasticity with the support of a high rate of network technical resources utilization can be achieved using virtualization technologies and the concept of network slicing, that allows deploying dedicated virtual networks based on services, using the methods of network functions virtualization. In this context, the main question remains what number of network slices will be feasible in terms of the cost of slice managing and providing the required functionality. Objective. To improve the efficiency of mobile network through optimal slice forming in multiservice telecommunication network. Method. Analysis of the publications devoted to network functions virtualization has shown the organization approach of using the multiservice mobile network resources, and also showed insufficient efficiency of existing solutions on important issues of this process (the performance of the slices formation algorithm, the level of functional costs). Results. A method to model network resource allocation system, which forms dedicated network slices that serve certain types of services independently on the common infrastructure. Conclusions. The article solves the problem of constructing a method for forming multiservice core mobile communication network slices, which, due to the effective dynamic configuration of the system, allows providing a wide range of services with a given quality indicators. A model of resource management system is constructed, which is used to present a method that takes into account costs associated with excessive resource allocation, and also reduces the number of network configuration recalculations, which allows achieving a rational ratio of management costs and Quality of Service

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

Towards service-oriented soft spectrum slicing for 5G TDD networks

This paper introduces the concept of network slicing in the emerging Time Division Duplex (TDD) 5G networks, considering asymmetric traffic conditions. The proposed approach relies on a flexible—soft spectrum—slicing considering the entire spectrum band by allocating service oriented network slices that can independently adjust the Uplink/Downlink (UL/DL) ratio based on forecasted traffic and user mobility information. Two novel resource re-configuration mechanisms are introduced: i) a joint slice resource allocation and TDD frame selection and ii) a slice allocation with independent distributed UL/DL ratio adjustment. An analytical model formulating the slice resource allocation scheme as a weighted optimization problem is presented whereas an exhaustive simulation campaign is carried out to validate our proposals against state-of-the art approaches.

Supporting QoS requirements provisions on 5G network slices using an efficient priority-based polling technique

Mobile operators aim to enhance the Quality of Service (QoS) in each new mobile network generation. The upcoming 5G mobile network is expected to make a revolutionary enhancement in the provided service level. Serving diverse requirements from different mobile network users in a flexible and a dynamic way is a key demand. Thus, the 5G network-slicing concept provides an optimal and an efficient way to deal with the diversity of end users requirements and demands. In 5G, three different network slices share the network resources in a virtually isolated slicing manner. These slices will be in different priority classes. Indeed, priority scheduling algorithms are needed to guarantee the service level for each slice among all other lesser priority slices. This paper proposes a priority-based polling scheme with multiple scheduling techniques for the data traffic coming from multiple 5G network slices. Cyclic and random polling with/without priority are analyzed and discussed for multiple 5G use cases. The analytical model of the proposed priority-based polling scheme is presented and the performance measures in terms of the average waiting time and utilization are derived. The results prove that the proposed polling scheme shows a powerful ability to control the 5G virtual base-band units pool and guard the QoS of the 5G slices, which is what 5G networks need.

A Survey of Network Slicing in 5G

The traditional network is facing many problems, making its development in the bottleneck period. Data forwarding efficiency and resource utilization rate are very low, the structure is also complex, and the redundancy is not optimized[1]. In order to solve this problem, communication operators continue to develop communication technology, which is the fifth generation of mobile communication technology, then has been introduced the concept of network slicing, putting the network function virtualization and software defined networks into the network slicing[2]. The new network slicing technology in this paper can improve the network service capability and meet the needs of multi service development.