Network Slicing Research Articles

A Carrying Method for 5G Network Slicing in Smart Grid Communication Services Based on Neural Network

When applying 5G network slicing technology, the operator’s network resources in the form of mutually isolated logical network slices provide specific service requirements and quality of service guarantees for smart grid communication services. In the face of the new situation of 5G, which comprises the surge in demand for smart grid communication services and service types, as well as the digital and intelligent development of communication networks, it is even more important to provide a self-intelligent resource allocation and carrying method when slicing resources are allocated. To this end, a carrying method based on a neural network is proposed. The objective is to establish a hierarchical scheduling system for smart grid communication services at the power smart gate-way at the edge, where intelligent classification matching of smart grid communication services to (i) adapt to the characteristics of 5G network slicing and (ii) dynamic prediction of traffic in the slicing network are both realized. This hierarchical scheduling system extracts the data features of the services and encodes the data through a one-dimensional Convolutional Neural Network (1D CNN) in order to achieve intelligent classification and matching of smart grid communication services. This system also combines with Bidirectional Long Short-Term Memory Neural Network (BILSTM) in order to achieve a dynamic prediction of time-series based traffic in the slicing network. The simulation results validate the feasibility of a service classification model based on a 1D CNN and a traffic prediction model based on BILSTM for smart grid communication services.

Optimal radio resource management in 5G NR featuring network slicing

3GPP 5G New Radio (NR) has introduced several new features that the network slicing concept can leverage to guarantee the heterogeneous requirements in terms of throughput and delays of expected 5G network services. Mainly, these features are (i) Mixed-numerology to control the time slot duration, hence guaranteeing low latency requirements; (ii) Bandwidth Parts (BWP) to control the number of radio resources allocated to users, hence satisfying different throughput requirements. However, efficient radio resource management is already complex, and adding these new dimensions will further increase this complexity. In this paper, we first propose modeling radio resource management in 5G NR featuring network slicing through a Mixed Integer Linear Program (MILP). For our best knowledge, this is the first MILP modeling of the radio resource management featuring network slicing taking into account (i) Mixed-numerology, (ii) both latency and throughput requirements (iii) multiple slice attach per UE (iv) Inter-Numerology Interference (INI). After showing that solving the problem takes an exponential time, we considered a new approach to solve it in a polynomial time, which is highly required when scheduling radio resources. The new approach consists of formalizing this problem using a Deep Reinforcement Learning (DRL)-based solver. We evaluate the use of RL to solve the problem for different network configurations and compared its performance with the optimal solution obtained by solving the MILP problem.

Cross-Chain Trusted Service Quality Computing Scheme for Multichain-Model-Based 5G Network Slicing SLA

Cross-Chain Trusted Service Quality Computing Scheme for Multichain-Model-Based 5G Network Slicing SLA

An intelligent fuzzy-based system for handover decision in 5G-IoT networks considering network slicing and SDN technologies

By enabling 5G technology, IoT networks can improve the performance of connected IoT devices. However, handover, ping-pong effect and load balancing are critical issues because of massive and unplaned deployment of small cells. The handover operation in 5G wireless networks is complicated because there are many radio access techniques and technologies. Therefore, the mobility management is required to provide QoS for various applications. In this paper, we present a fuzzy-based system for handover decision in 5G wireless networks considering different network slicing parameters. We implement two Fuzzy-based Handover Decision Models (FHDM): FHDM1 and FHDM2. We evaluate the proposed models by simulations. For FHDM1, Slice Delay (SD), Slice Bandwidth (SB), and Slice Load (SL) are three input parameters and Handover Decision (HD) is the output parameter. For FHDM2, Slice Reliability (SR) is a new considered parameter, so it has four input parameters and the output parameter is HD the same as FHDM1. We found from simulation results that the considered parameters have different effects on HD. For both models, the HD value increases as SD and SL values increase while HD value is decreasing when SB and SR values increase. The results of the simulation indicate that although FHDM2 is more complex than FHDM1, it performs better HD since considers four input parameters. From the simulation results of FHDM2, when we changed SD value form 10% to 90%, we found that the HD value is increased by 50% when SR is 50% and SL is 90%. This indicates that when a user is connecting with the present slice that has bad QoS, it should handover to another slice with better QoS.

Cost-efficient slicing in virtual Radio Access Networks

Network slicing is a promising technique that has vastly increased the manifoldness of network services to be supported through isolated slices in a shared radio access network (RAN). Due to resource isolation, effective resource allocation for coexisting multiple network slices is essential to maximize network resource efficiency. However, the increased network flexibility and programmability offered by virtualized radio access networks (vRANs) come at the expense of a higher consumption of computing resources at the network edge. Additionally, the relationship between resource efficiency and computing cost minimization is still fuzzy. In this paper, we first perform extensive experiments using the vRAN testbed we developed and assess the vRAN resource consumption under different settings and a varying number of users. Then, leveraging our experimental findings, we formulate the problem of cost-efficient network slice dimensioning, named cost-efficient slicing (CES), which maximizes the difference between total utility and CPU cost of network slices. Numerical results confirm that our solution leads to a cost-efficient resource slicing, while also accomplishing performance isolation and guaranteeing the target data rate and delay specified in the service level agreements.

Towards Enabling Haptic Communications over 6G: Issues and Challenges

This research paper provides a comprehensive overview of the challenges and potential solutions related to enabling haptic communication over the Tactile Internet in the context of 6G networks. The increasing demand for multimedia services and device proliferation has resulted in limited radio resources, posing challenges in their efficient allocation for Device-to-Device (D2D)-assisted haptic communications. Achieving ultra-low latency, security, and energy efficiency are crucial requirements for enabling haptic communication over TI. The paper explores various methodologies, technologies, and frameworks that can facilitate haptic communication, including backscatter communications (BsC), non-orthogonal multiple access (NOMA), and software-defined networks. Additionally, it discusses the potential of unmanned aerial vehicles (UAVs), network slicing, and wireless communication beyond 100 GHz and THz levels in improving haptic communication performance. The research emphasizes the importance of addressing security risks, optimizing resource allocation, and minimizing network congestion to unlock the potential of future networks and services. Aerial, ground, and underwater communication technologies are highlighted as key components of 6G networks, each with their advantages and challenges. The need for specialized equipment in remote areas to meet the bandwidth and latency requirements of haptic communication is underscored. The findings of this research contribute to a deeper understanding of haptic communication in the context of 6G networks and provide insights into potential solutions for overcoming the associated challenges.

5G in healthcare: Matching game-empowered intelligent medical network slicing

Improving the healthcare system is imperative for increasing efficiency and reducing costs. The rapid adoption of Internet of Medical Things (IoMT) has facilitated a broad range of healthcare applications and services, from real-time and critical care monitoring to telemedicine. These services typically have highly distinct quality of service (QoS) requirement for communication networks and thus cannot be served with a uniform network. In such a context, network slicing technologies in 5G can be employed to create virtually independent and customized communication networks for these use cases to meet their QoS requirement. In this wok, we model network slicing for healthcare services as a virtual network embedding (VNE) problem and propose a two-sided matching theory-based virtual network embedding (MT-VNE) solution. In MT-VNE, four novel preference indexes are devised to construct differential preference lists. The deferred acceptance and modified shortest path-based algorithms are utilized to perform the virtual node and link mapping, respectively. Extensive simulations demonstrate that MT-VNE outperforms other baselines in terms of accepting more healthcare services and effectively utilizing physical network resources. Moreover, MT-VNE also significantly reduces service embedding time.

MULTI-ACCESS EDGE COMPUTING ANALYSIS OF RISKS AND SECURITY MEASURES

5G efficiently uses technologies like network slicing (NS), network function virtualization (NFV), software-defined network (SDN), and multi-access edge computing (MEC). At the same time, embracing these technologies and creating new services opens the network to a new set of security challenges. This paper presents a threat analysis of MEC features. The paper's novelty resides in viewing MEC as common ground for the telco and IT sectors. This paper studies the measures according to ISO/IEC 27001:2022 controls. ISO/IEC 27001 is the most popular standard for information security management systems, with a new version published in 2022.

SNAF: DRL-Based Interdependent E2E Resource Slicing Scheme for a Virtualized Network

The fifth generation (5G) mobile cellular network relies on network slicing (NS) to satisfy the diverse quality of service (QoS) requirements of various service providers operating on a standard shared infrastructure. However, the synchronization of radio access network (RAN) and core network (CN) slicing has not been well-studied as an interdependent resource allocation problem. This work proposes a novel slice-to-node access factor (SNAF)-based end-to-end (E2E) slice resource provisioning scheme and deep reinforcement learning (DRL)-based real-time resource allocation algorithm for E2E interdependent resource slicing and allocation, respectively, specifically for RAN and CN. To ensure effective resource slicing and allocation, we consider the versatile user equipment (UEs) QoS requirements on transmission delay and data rate. Notably, the SNAF-based scheme provides proper resource provisioning and traffic synchronization, while the DRL-based algorithm allocates radio resources based on affordable traffic and backhaul resources. Based on the 5G air interface, we conduct system-level simulations to evaluate the performance of our proposed methods from various perspectives. Simulation results confirm that our proposed SNAF and DRL-based interdependent E2E resource slicing and allocation techniques achieve better E2E traffic-resource synchronization, and improve the QoS satisfaction with minimal resource utilization compared to other existing benchmark schemes.

Multi-Layer 5G Network Slicing with UAVs: An Optimization Model

In this paper, we present a network-based optimization model describing a closed-loop supply chain for the provision of 5G network slices on demand to users and devices on the ground. The three-tier supply chain network consists of a fleet of pre-existing UAVs, to which others can be added, managed by a fleet of UAV controllers, whose purpose is to perform services requested by users and devices on the ground. The aim of this paper is to provide a constrained optimization problem through which the providers’ profits are maximized, determining the global optimal distributions of request flows, the global optimal distributions of executed services and the optimal reliability level of pre-existing UAVs of the fleet. We also derive the associated Variational inequality formulation of the problem and, finally, a numerical simulation is performed to validate the effectiveness of the model.

Dual-Band Aerial Networks for Priority-Based Traffic

Unmanned aerial vehicle networks suffer from lackluster performance due to line-of-sight issues as well as resource scarcity. Network slicing, multi-band transmission, and numerology show great potential in mitigating such limitations. In this work, we propose the use of the sub-6 GHz and mmWave bands, the latter of which requires careful consideration of line-of-sight, with numerology for aerial network slicing to provision time-critical services and broadband access. Accordingly, we formulate a user admission control policy to regulate band access after which we formulate a joint resource block and power allocation problem, a mixed-integer non-linear programming problem, to minimize the quality-of-service gap of the throughput-dependent broadband users as a best-effort service and meet the time-critical service requirements. We propose a low-complexity algorithm, PREDICT, to tackle the formulated problem and present extensive simulation results to validate the advantages of our scheme.

Virtual Service Embedding With Time-Varying Load and Provable Guarantees

Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual network functions (VNFs) at different locations of the cellular infrastructure and manage such instances by scaling them as needed. In this work, we address the above problem and the very relevant aspect of sub-slice reuse among different services. Further, unlike prior art, we account for the services' finite lifetime and time-varying traffic load. We identify two major sources of inefficiency in service management: (i) the overspending of computing resources due to traffic of multiple services with different latency requirements being processed by the same virtual machine (VM), and (ii) the poor packing of traffic processing requests in the same VM, leading to opening more VMs than necessary. To cope with the above issues, we devise an algorithm, called REShare, that can dynamically adapt to the system's operational conditions and find an optimal trade-off between the aforementioned opposite requirements. We prove that REShare has low algorithmic complexity and is asymptotic 2-competitive under a non-decreasing load. Numerical results, leveraging real-world scenarios, show that our solution outperforms alternatives, swiftly adapting to time-varying conditions and reducing service cost by over 25%.

Make Rental Reliable: Blockchain-Based Network Slice Management Framework with SLA Guarantee

Make Rental Reliable: Blockchain-Based Network Slice Management Framework with SLA Guarantee

Efficient Network Slicing with SDN and Heuristic Algorithm for Low Latency Services in 5G/B5G Networks.

This paper presents a novel approach for network slicing in 5G backhaul networks, targeting services with low or very low latency requirements. We propose a modified A* algorithm that incorporates network quality of service parameters into a composite metric. The algorithm's efficiency outperforms that of Dijkstra's algorithm using a precalculated heuristic function and a real-time monitoring strategy for congestion management. We integrate the algorithm into an SDN module called a path computation element, which computes the optimal path for the network slices. Experimental results show that the proposed algorithm significantly reduces processing time compared to Dijkstra's algorithm, particularly in complex topologies, with an order of magnitude improvement. The algorithm successfully adjusts paths in real-time to meet low latency requirements, preventing packet delay from exceeding the established threshold. The end-to-end measurements using the Speedtest client validate the algorithm's performance in differentiating traffic with and without delay requirements. These results demonstrate the efficacy of our approach in achieving ultra-reliable low-latency communication (URLLC) in 5G backhaul networks.

Research on 5G Network Slicing Strategy for Urban Complex Environment

Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.

Strengthening network slicing for Industrial Internet with deep reinforcement learning

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain. Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology. Guaranteeing robust network slicing is essential for Industrial Internet, but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions (NFs) composing the slice. Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties. Towards this end, we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements. State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution. Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.

AN INTELLIGENT NETWORK SLICING FRAMEWORK FOR DYNAMIC RESOURCE SHARING IN MULTI-ACCESS EDGE COMPUTING ENABLED NETWORKS

Dynamic resource sharing in multi-access edge computing (MEC) enabled networks has gained tremendous interest in the recent past, paving the way for the realization of beyond fifth generation (B5G) communication networks. To enable efficient and dynamic resource sharing, Network Slicing has appeared as a promising solution, virtualizing the network resources in the form of multiple slices employed by the end-users requiring strict latency, proximate computations, and storage demands. In literature, network slicing is primarily studied in the context of communication resource slicing, and little research has been devoted to jointly slicing communication, energy, and MEC resources. In this paper, we, therefore, proposed a joint network-slicing framework that considers 1) communication resources, 2) compute resources, 3) storage resources, and 4) energy resources, and intelligently and dynamically shares the resources between different slices, aiming to improve tenants' overall utility. To this end, we formulated a utility maximization problem as Markov-chain Decision Process. We utilized a tenant's manager that employs a deep reinforcement learning technique named "deep deterministic policy gradient" to enable dynamic resource sharing. Simulation results reveal the effectiveness of the proposed scheme.

Dynamic Selection Slicing-Based Offloading Algorithm for In-Vehicle Tasks in Mobile Edge Computing

With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.

DTN-Assisted Dynamic Cooperative Slicing for Delay-Sensitive Service in MEC-Enabled IoT via Deep Deterministic Policy Gradient With Variable Action

Network Slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with Multi-access Edge Computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this paper, we propose a paradigm of dynamic cooperative slicing based on Digital Twin Network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models including prediction and decision-making in DTN. Then we realize dynamic cooperative slicing through the built basic models and function models. Secondly, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Thirdly, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a Deep Deterministic Policy Gradient algorithm with Variable Action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

Service-Based Management Architecture for On-Demand Creation, Configuration, and Control of a Network Slice Subnet

Service-Based Management Architecture for On-Demand Creation, Configuration, and Control of a Network Slice Subnet