Network Slicing Research Articles

Network Slicing Resource Allocation Optimization Based on Multiactor-Attention-Critic Joint With Bidding in Heterogeneous Integrated Network

Network Slicing Resource Allocation Optimization Based on Multiactor-Attention-Critic Joint With Bidding in Heterogeneous Integrated Network

Intelligent Network Slicing for B5G and 6G: Resource Allocation, Service Provisioning, and Security

Intelligent Network Slicing for B5G and 6G: Resource Allocation, Service Provisioning, and Security

Improvement of 5G Core Network Performance using Network Slicing and Deep Reinforcement Learning

Users have increasingly been having more use cases for the network while expecting the best Quality of Service (QoS) and Quality of Experience (QoE). The Fifth Generation of mobile telecommunications technology (5G) network had promised to satisfy most of the expectations and network slicing had been introduced in 5G to be able to satisfy various use cases. However, creating slices in a real-life environment with just the resources required while having optimized QoS has been a challenge. This has necessitated more intelligence to be required in the network and machine learning (ML) has been used recently to add the intelligence and ensure zero-touch automation. This research addresses the open question of creating slices to satisfy various use cases based on their QoS requirements, managing, and orchestrating them optimally with minimal resources while allowing the isolation of services by introducing a Deep reinforcement Machine Learning (DRL) algorithm. This research first evaluates the previous work done in improving QoS in the 5G core. 5G architecture is simulated by following the ETSI NFV MANO (European Telecommunications Standards Institute for Network Function Virtualization Management and Orchestration) framework and uses Open5G in 5G core, UERANISM for RAN, Openstack for Virtual Infrastructure Manager (VIM), and Tacker for Virtual Network Function Management and orchestration (VNFMO). The research simulates network slicing at the User Plane Function (UPF) level and evaluates how it has improved QoS. The network slicing function is automated by following ETSI Closed Loop Architecture and using Deep Reinforcement Learning (DRL) by modeling the problem as a Markov Decision Problem (MDP). Throughput is the Reward for the actions of the DRL agent. Comparison is done on the impact of slicing on throughput and compares models that have not been sliced, the ones that have been sliced and combined to work together, and models with slices that have been assigned more bandwidth. Sliced networks have better throughput than the ones not sliced. If more slices are load-balanced the throughput is increased. Deep Reinforcement Learning has managed to achieve the dynamic assigning of slices to compensate for declining throughput.

Exploring the effectiveness of service migration strategies for virtual network embedding

Network slicing, a key component of post-5G networks, has brought virtual network embedding (VNE) to the forefront of networking research. However, existing VNE approaches are limited by their consideration of static network topologies, failing to account for the dynamic nature of real-world networks. This limitation becomes particularly problematic in the face of link failures or topology modifications, rendering these approaches ineffective. To address this, we propose a new service placement strategy that remains effective even when the network topology changes. Furthermore, we introduce several service migration strategies and thoroughly investigate their effectiveness. Our results demonstrate the adaptability of our proposed strategy, which leverages graph neural networks, in handling link failures without necessitating relearning. This adaptability underlines the potential of our approach to significantly enhance the robustness and flexibility of service migration in VNE, thereby contributing to the evolution of network slicing in post-5G networks.

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

Overhead and Performance of Dynamic Network Slice Allocation for Mobile Users

Based on virtualisation technologies like SDN and NFV, modern networks like 5G create customised logical networks known as Network Slicing to serve distinct user groups. The mobility of a significant part of these users brings great challenges for that scenario, e.g. demand variations and continuous connectivity. In such a scenario, the dynamic resource allocation of these slices is shown as a possible solution. However, that approach may be sensitive to computing overhead and trade-off scenarios, which are not fully evaluated in the literature. This work focuses on that gap by researching the impact of network slice reconfiguration overhead on the overall network performance. An extensive evaluation of Dynamic Network Slicing in providing user mobility support in Fog computing-based architectures is presented. This paper presents a resource allocation model that calculates the required time to allocate a slice instance. Furthermore, based on those values, this work presents an evaluation of the impact of resource allocation delays in terms of bandwidth allocation, latency, and service outage in different dynamic slicing scenarios that include service migration. Simulations based on realistic values point out that reducing the allocation delay from 60 to 30 s can lead the network to 25% more stable latency to its users in 60 min slice lifetime.

Economic Alternatives for the Provision of URLLC and eMBB Services Over a 5G Network

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

An architecture for control plane slicing in beyond 5G networks

To accommodate various use cases with differing characteristics, the Fifth Generation (5G) mobile communications system intends to utilize network slicing. Network slicing enables the creation of multiple logical networks over a shared physical network infrastructure. While the problems such as resource allocation for multiple slices in mobile networks have been explored in considerable detail in the existing literature, the suitability of the existing mobile network architecture to support network slicing has not been analysed adequately. We think the existing 5G System (5GS) architecture suffers from certain limitations, such as a lack of slice isolation in its control plane. This work focuses on the future evolution of the Third Generation Partnership Project (3GPP) 5GS architecture from a slicing perspective, especially that of its control plane, addressing some of the limitations of the 3GPP 5GS architecture. We propose a new network architecture which enables efficient slicing in beyond 5G networks. The proposed architecture results in enhanced modularity and scalability of the control plane in sliced mobile networks. In addition, it also brings slice isolation to the control plane, which is not feasible in the existing 5G system. We also present a performance evaluation that confirms the improved performance and scalability of the proposed system viz-a-viz the existing 5G system.

Towards efficient RAN slicing: A deep hierarchical reinforcement learning approach

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms.

Oneshot Deep Reinforcement Learning Approach to Network Slicing for Autonomous IoT Systems

Oneshot Deep Reinforcement Learning Approach to Network Slicing for Autonomous IoT Systems

Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey

Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.

Adaptive Methods for Revenue Model Learning of a Slice Broker in the Presence of Adversaries

AbstractIn the fifth-generation (5G) of mobile networks, Multi-Access Edge Computing (MEC) refers to the deployment of computing resources closer to the end-users for improved service delivery. In the context of 5G MEC, the slice broker plays a crucial role in managing the allocation of resources among the different network slices, which are logical networks on top of a shared infrastructure. The slice broker is a business entity that acts as an intermediary between the slice tenants and the infrastructure provider and is responsible for allocating resources (such as CPU, memory, and network bandwidth) required to set up the network. The slice broker must ensure that resources are allocated in a way that the revenue is maximized. In a dynamic environment, the slice broker must learn the revenue model adaptively and online. Adversaries can significantly reduce the revenue by misleading the system about the resources pretending to be selfish nodes, or creating noise. The slice broker should learn the revenue model in the presence of adversaries. We apply cooperative deep reinforcement learning with consensus mechanism and consensus deep learning to learn the revenue model adaptively. We also compare our proposed methods with the reference solution. Simulation results show that our proposed methods, especially the cooperative version, outperform the reference solution.

Temporal Characterization and Prediction of VR Traffic: A Network Slicing Use Case

Temporal Characterization and Prediction of VR Traffic: A Network Slicing Use Case

Intelligent Hierarchical NOMA-Based Network Slicing in Cell-Free RAN for 6G Systems

Intelligent Hierarchical NOMA-Based Network Slicing in Cell-Free RAN for 6G Systems

DDPG 및 연합학습 기반 5G 네트워크 자원 할당과 트래픽 예측

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

Cost-efficient RAN slicing for service provisioning in 5G/B5G

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

Correction to “Multi‐objective metaheuristic optimization‐based clustering with network slicing technique for internet of things‐enabled wireless sensor networks in 5G systems”

Correction to “Multi‐objective metaheuristic optimization‐based clustering with network slicing technique for internet of things‐enabled wireless sensor networks in <scp>5G</scp> systems”

Network slicing: Is it worth regulating in a network neutrality context?

Network slicing is a key component of 5G-and-beyond networks but induces many questions related to an associated business model and its need to be regulated due to its difficult co-existence with the network neutrality debate. We propose in this paper a slicing model in the case of heterogeneous users/applications where a service provider may purchase a slice in a wireless network and offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. We obtain thanks to game theory the economically-optimal slice size and prices charged by all actors. We also compare with the case of a unique “pipe” (no premium service) corresponding to a fully-neutral scenario and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario and the eventual need for regulation.

Resource Allocation for LoRaWAN Network Slicing: Multi-Armed Bandit-based Approaches

Wireless sensor networks have become increasingly popular in recent years due to the growing demand for Internet of Things (IoT) applications, including LoRaWAN networks. However, the effective allocation of resources remains a crucial challenge in LoRaWAN networks due to the limited bandwidth and the diverse demands for multiple services. This paper presents three novel resource allocation solutions for LoRaWAN network slicing to address this challenge. These solutions are based on the Multi-Armed Bandit (MAB) algorithm, which is known for balancing the exploration of available actions with the exploitation of optimal decisions. Our objective is to dynamically and efficiently allocate resources to network slices by treating the resource allocation as a MAB problem. This approach aims to maximize Packet Delivery Rate (PDR) performance while ensuring each service’s Service Level Agreement (SLA). The first solution, UCB-MAB, uses the Upper Confidence Bound (UCB) strategy to balance exploration and exploitation to improve network performance. The second solution, Q-UCB-MAB, continuously updates Q-values using the Q-learning update equation and incorporates the UCB strategy for further optimization. Finally, the third solution, ARIMA-UCB-MAB, leverages the predicted reward value from the Autoregressive Integrated Moving Average (ARIMA) model within the UCB framework to enhance network performance. Our results demonstrate that all three solutions offer efficient resource allocation in terms of PDR and SLA satisfaction. Specifically, the ARIMA-UCB-MAB solution outperforms the two other solutions.