Network Slicing Research Articles

A Nested Decomposition Model for Reliable NFV 5G Network Slicing

A Nested Decomposition Model for Reliable NFV 5G Network Slicing

Service Function Chaining and Embedding With Heterogeneous Faults Tolerance in Edge Networks

In the 5G-and-beyond era, ultra-reliable low latency communication (URLLC) services are ubiquitous in edge networks. To enhance the performance metrics and the quality of service (QoS), URLLC services are delivered via a sequence of software-based network functions, also known as a service function chain (SFC). Towards reliable SFC delivery, it is imperative to incorporate fault-tolerance during SFC deployments. However, deploying an SFC with fault-tolerance is challenging because the protection mechanism needs to jointly consider multiple concurrent physical/virtual network failures and hardware/software failures. Considering these concurrent heterogeneous failures, this work investigates how to effectively deliver an SFC in edge networks with the objective of minimizing bandwidth resource consumption. First, we introduce the concept of k-heterogeneous-faults-tolerance and propose an augmented protection graph, called k-connected service function slices layered graph (KC-SLG). Based on the KC-SLG, we formulate a novel problem called k-heterogeneous-faults-tolerant SFC embedding and propose an effective algorithm, called fault-tolerant service function graph embedding (FT-SFGE). FT-SFGE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Via thorough mathematical proofs, we show that KC-NS is 2-approximate. Extensive simulations show that KC-FS has the best average cost-efficiency when k = 2, and FT-SFGE outperforms the schemes directly extended from the state-of-the-art.

Deep Reinforcement Learning for NextG Radio Access Network Slicing With Spectrum Coexistence

Reinforcement learning (RL) is applied for dynamic admission control and resource allocation in NextG radio access network slicing. When sharing the spectrum with an incumbent user (that dynamically occupies frequency-time blocks), communication and computational resources are allocated to slicing requests, each with priority (weight), throughput, latency, and computational requirements. RL maximizes the total weight of granted requests over time beyond myopic, greedy, random, and first come, first served solutions. As the state-action space grows, Deep Q-network effectively admits requests and allocates resources as a low-complexity solution that is robust to sensing errors in detecting the incumbent user activity.

Modal-Aware Resource Allocation for Cross-Modal Collaborative Communication in IIoT

With the development of human-machine interactions, users are increasingly evolving towards an immersion experience with multi-dimensional stimuli. Facing this trend, cross-modal collaborative communication is considered an effective technology in the Industrial Internet of Things (IIoT). In this paper, we focus on open issues about resource reuse, pair interactivity, and user assurance in cross-modal collaborative communication to improve quality of service (QoS) and users’ satisfaction. Therefore, we propose a novel architecture of modal-aware resource allocation to solve these contradictions. First, taking all the characteristics of multi-modal into account, we introduce network slices to visualize resource allocation, which is modeled as a Markov Decision Process (MDP). Second, we decompose the problem by the transformation of probabilistic constraint and Lyapunov Optimization. Third, we propose a deep reinforcement learning (DRL) decentralized method in the dynamic environment. Meanwhile, a federated DRL framework is provided to overcome the training limitations of local DRL models. Finally, numerical results demonstrate that our proposed method performs better than other decentralized methods and achieves superiority in cross-modal collaborative communications.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Salience-based VNF placement method for evolving multiple network slices

Network Function Virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One of the parameters that highly influences radio resource management is the profile of user traffic generated by various 5G applications. Forecasting such mobile network profiles helps with numerous RRM tasks such as network slicing and load balancing. In this paper, we analyze a dataset from a mobile network operator in Portugal that contains information about volumes of traffic in download and upload directions in one-hour time slots. We apply two statistical models for forecasting download and upload traffic profiles, namely, seasonal autoregressive integrated moving average (SARIMA) and Holt-Winters models. We demonstrate that both models are suitable for forecasting mobile network traffic. Nevertheless, the SARIMA model is more appropriate for download traffic (e.g., MAPE [mean absolute percentage error] of 11.2% vs. 15% for Holt-Winters), while the Holt-Winters model is better suited for upload traffic (e.g., MAPE of 4.17% vs. 9.9% for SARIMA and Holt-Winters, respectively).

Analysis of feasible region in network slicing: Markov chain approach

Network slicing is a key feature introduced for 5G and Beyond 5G (B5G) to provide complete connectivity between people and things by overcoming limited resources. This study considers a theoretical approach for resource allocation when network slicing is employed. Specifically, an algorithm finding a feasible region of users for a given environment is proposed, and a continuous time Markov chain is adopted for its analysis. Some performance measures, such as average throughput and expected total revenue of the infrastructure provider, are expressed by the stationary probabilities of the Markov chain. A simple example and three different environments are presented in the experiment to illustrate the analysis procedure and investigate the effects of environments on performance, respectively. This analytical approach can simplify and speed up the evaluation of the performance of the network operator's strategy and increases the reliability of the evaluation, which is beneficial in practical applications. Moreover, this can provide theory based-guidance for infrastructure providers in determining the consistent network slice allocation policies, considering the users' needs and the network operator's decision strategy.

Toward Resilient Network Slicing for Satellite–Terrestrial Edge Computing IoT

Satellite-Terrestrial Edge Computing Networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs such as Software-Defined Networking (SDN), satellite edge computing, and Network Function Virtualization (NFV) are key to realizing this vision. In this paper, we survey and analyze network slicing solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite edge computing, mmWave/THz, and AI solutions to make network slicing adaptive. In addition, we identify challenges and open issues to slice STECNs. In particular, resilient network slicing is crucial for essential and critical services. Network failures are unavoidable in large networks and can cause significant disruptions in network slicing, compromising many services. To this end, we present a resilient network slicing design to cope with failures and guarantee service continuity which is agnostic to the integration architecture and inherently multi-domain. Further, we present strategies to achieve resilient networking and slicing in STECNs including planning and provisioning of redundant network resources, design rules for service level agreement decomposition, and cross-domain solutions to detect and mitigate failures. Finally, promising future research directions are highlighted. This paper provides valuable guidelines for slicing STECNs and will benefit key sectors, such as smart healthcare, e-commerce, industrial IoT, and education, among others.

Security-aware resource management approaches in software defined networks: Comprehensive analysis, opportunities and challenges

Today, with the fast growth of emerging technologies and applying numerous advantages of 5G communications, there is a critical gap between the supporting security of data transmission and resource management of Software Defined Networks (SDN). To provide a secure communication perspective for Internet of Things (IoT) devices and smart applications, network slicing in resource management is currently an important issue and is widely studied for the SDN. Due to the importance of security-aware resource management in several aspects of the SDN, this research aims to represent a new comprehensive review of existing technical classification and deep-detailed categorization of resource management approaches for SDN communications in the IoT environments. Based on each category, a technical taxonomy is presented to show a variety of security-aware platforms that include resource allocation, service composition, task scheduling, and service offloading in the IoT environments. According to the main state-of-the-art discussed comprehensive technical review, some important evaluation factors, main methodologies, advantages, and disadvantages of each case study are elaborated. Finally, some important new research directions and open challenges are presented for future scientific efforts.

URLLC-eMBB hierarchical network slicing for Internet of Vehicles: An AoI-sensitive approach

Network slicing has been considered as a promising candidate to meet the heterogeneous Quality of Service (QoS) requirements of various vehicular applications. In this paper, network slicing is utilized on the Road Side Unit (RSU) side for the heterogeneous data freshness requirements in Internet of Vehicles (IoV). The services in enhanced Mobile BroadBand (eMBB) slice are usually bandwidth-consuming, thus the performance can be characterized by throughput. Unlike eMBB slice, the services in Ultra-Reliable Low-Latency Communications (URLLC) slice are usually time-critical, calling for fresh content delivery within short delay, in this case, the trade-off can be made between Age of Information (AoI) and delay in URLLC slice. The resource limitation and coupling effect at both the inter-slice and the intra-slice level have brought challenges to the resource allocation of vehicular network slice. To this end, this paper models the inter- and intra-slice resource allocation problem as a Stackelberg game, in which the RSU acts as the leader and the two slices act as followers. To minimize the weighted sum of AoI and delay in URLLC slice while ensuring the throughput of eMBB slice, this paper proposes a multi-win Stackelberg game resource allocation (MWRA) algorithm. The proposed algorithm can achieve Stackelberg equilibrium (SE) and the SE point is taken as the optimal resource allocation strategy of inter- and intra-slice. Simulation shows the trade-off of inter- and intra-slice resource allocation, and further reveals the interaction of performance between different slices. Numerical results show that compared with the traditional hard slicing, the proposed algorithm can improve the AoI and delay performance of URLLC slice by about 30% under the premise of ensuring the throughput of eMBB slicing.

An auction-based distributed network slicing scheme for resource allocation in satellite-UAV integrated networks

Network slicing is a promising solution to satisfy different service quality requirements in the satellite-UAV integrated network of B5G and 6G. To relieve the significant transmission delay and system overhead during network slicing, we propose an auction-based distributed network slicing scheme (ADNSS), in which the resource allocation is collaboratively performed by satellite heads using an auction algorithm without transmitting the messages to the ground station in a centralized way. The convergence and service delay performance of ADNSS is analyzed in this paper. Moreover, considering that the deviation of theoretical slice analysis models from actual ones may lead to the degradation of service delay performance of ADNSS, we design an online optimization method driven by the multi-arm bandit algorithm (ORLOM) to enhance the accuracy of slice models of ADNSS in the actual environment. Numerical results show that we can achieve optimal service delay performance by ADNSS applying ORLOM after finite slicing windows and realize 8% performance improvement compared with ADNSS without ORLOM when the service number is 9. In addition, ADNSS can reduce the system overhead by 8.5%–76% compared with the centralized method in the scenario where the hops count from satellite heads to the ground station exceeds that between satellite heads by 3. As the hops count difference and service number increase, ADNSS shows more significant advantages in reducing service delay and system overhead.

DRL-based admission control and resource allocation for 5G network slicing

DRL-based admission control and resource allocation for 5G network slicing

Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review.

The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike.

Handoff Control and Resource Allocation for RAN Slicing in IoT Based on DTN: An Improved Algorithm Based on Actor–Critic Framework

As a three-layer association of Internet of things equipment (IoTE) -network slicing (NS) -base station (BS) in radio access network (RAN) slicing, handoff control and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing are investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then an improved algorithm based on actor-critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for Handoff Control and an optimizer for Resource Allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.

5G and Companion Technologies as a Boost in New Business Models for Logistics and Supply Chain

The transport and logistics industry plays a crucial role in supporting the economy, but it faces various challenges, including high costs and the need for operational efficiency. To address these challenges, the industry is embracing digital transformation, and 5G networks are expected to play a significant role in this process. This paper explores the benefits of 5G technologies in the transportation and logistics sector, focusing on device density, low latency, network slicing, supply chain visibility, port operations, and enhanced communication. Additionally, the paper emphasizes the importance of stakeholder engagement and sustainability considerations in the adoption of innovative technologies. The research methodology involves an online survey administered to stakeholders in the port logistics sector, aiming to assess their knowledge and implementation of innovative technologies. The paper also reviews the relevant literature and highlights the potential of digital technologies, such as IoT, blockchain, AI, and 5G, in optimizing supply chains and port operations. The findings provide insights into the current state of knowledge and implementation of innovative technologies in port operations and the potential for market adoption and contribute to understanding the benefits and challenges of 5G technology in the logistics industry.

Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-Agent Deep Reinforcement Learning Approach

In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices, guaranteeing the 5G-RAN quality of service. Moreover, to tackle connectivity interruptions during emergencies, UAVs have been deployed as airborne base stations, providing various services to ground networks. However, this increases the complexity of resource allocation in the shared infrastructure of 5G-RAN. Therefore, this paper proposes a dynamic resource allocation framework that synergies blockchain and multi-agent deep reinforcement learning for multi-UAV-enabled 5G-RAN to allocate resources to smart mobile user equipment (SMUE) with optimal costs. The blockchain ensures the security of virtual resource transactions between SMUEs, infrastructure providers (InPs), and virtual network operators (VNOs). We formulate a virtualized resource allocation problem as a hierarchical Stackelberg game containing InPs, VNOs, and SMUEs, and then transform it into a stochastic game model. Then, we adopt a Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve the formulated problem and obtain the optimal resource allocation policies that maximize the utility function. The simulation results show that the MADDPG method outperforms the state-of-the-art methods in terms of utility optimization and quality of service satisfaction.

Connectivity Maximization in Non-Orthogonal Network Slicing Enabled Industrial Internet-of-Things With Multiple Services

Industrial Internet of Things (IIoT) is a technological revolution that is profoundly reshaping the visage of industry. Facing the explosively increasing number of multi-service devices, traditional industrial network technology is no longer applicable. The advent of the fifth-generation (5G) wireless networks brings unprecedented possibilities for deploying the anticipated IIoT. To address the two main issues, i.e., connection density and multi-service requirements, in 5G empowered IIoT, we consider the non-orthogonal network slicing in this work. In particular, we jointly utilize network slicing to incorporate different types of services and exploit non-orthogonal multiple access (NOMA) to enhance the connection density. We formulate the connectivity maximization problem with joint sub-carrier association and power allocation as a mixed-integer nonlinear programming (MINLP). To tackle the intractable MINLP, we first transform it into a mixed-integer linear programming (MILP) and then simplify the MILP into an integer linear programming (ILP) by developing a simple yet effective pairing guideline. In order to further reduce the computational complexity, we then propose the alternating selection best-effort pairing (AS-BEP) algorithm with low complexity to solve the ILP effectively. Our analyses are supplemented by comprehensive simulation results that illustrate the performance superiority of the proposed algorithms to the benchmark schemes.

Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation

<p>To enhance the smart Cloud Service System for diverse user requirements in 5G and other service networks, this study leverages resource utilization and multi-tenancy network slicing operation costs. Specifically, we propose a multi-tenancy network resource allocation strategy based on the Chaos Particle Swarm Optimization (CPSO) algorithm. In a multi-tenancy network (MTN), we lease the wireless spectrum resources of the infrastructure provider’s base station, construct access service slices as network slice services, and offer network access services to users. Introduce detailed formulation of the relationship between MTN and users, represented as a multi-master and multi-slave construct that defines the strategy space and profit function after MTN decision-making. Reverse induction is used to analyze the proposed model, and a distributed iterative algorithm is proposed to obtain the optimal throughput demand of users and the optimal slice cost of MTN. Simulation results demonstrate that the proposed strategy can effectively enhance resource utilization and user satisfaction while reducing energy consumption.</p>

English

English