Network Slicing Research Articles

5G Vehicle-to-Everything at the Cross-Borders: Security Challenges and Opportunities

5G Vehicle-to-Everything (5G-V2X) communications will play a vital role in the development of the automotive industry. Indeed and thanks to the Network Slicing (NS) concept of 5G and beyond networks (B5G), unprecedented new vehicular use-cases can be supported on top of the same physical network. NS promises to enable the sharing of common network infrastructure and resources while ensuring strict traffic isolation and providing necessary network resources to each NS. However, enabling NS in vehicular networks brings new security challenges and requirements that automotive or 5G standards have not yet addressed. Attackers can exploit the weakest link in the slicing chain, connected and automated vehicles, to violate the slice isolation and degrade its performance. Furthermore, these attacks can be more powerful, especially if they are produced in cross-border areas of two countries, which require an optimal network transition from one operator to another. Therefore, this article aims to provide an overview of newly enabled 5G-V2X slicing use cases and their security issues while focusing on cross-border slicing attacks. It also presents the open security issues of 5G-V2X slicing and identifies some opportunities.

Mission Critical Communications Support With 5G and Network Slicing

Mission Critical (MC) communications take a pivotal role to achieve effective Public Protection and Disaster Relief (PPDR) actions. Even though 3GPP standards define MC applications and services in an architectural framework compatible with current 5G mobile networks, real-life experiments and applications of these concepts are still at the very beginning. In this paper, we present an architectural study and related experimental activity on network slicing for MC communications. We implemented these services in a fully virtualized environment, and deployed and tested them in a multi-domain network slicing scenario compliant with the ETSI NFV-MANO specifications. Our work aligns with the 5G approach separating control and data planes. The level of automation in service deployment and the slice isolation features are demonstrated, showing the benefits in terms of application performance, management flexibility, scalability, and quality of service differentiation capabilities.

DRL-Assisted Reoptimization of Network Slice Embedding on EON-Enabled Transport Networks

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations. A major challenge in supporting dynamic services is the lack of priori knowledge of future slice requests. As a consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. To address this issue, operators can periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but also offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint of limiting the number of re-configuration steps. Moreover, we present a novel method based on Deep Reinforcement Learning (DRL) for determining when performing re-configuration is most effective. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser re-configuration actions. Finally, we show that by applying the greedy algorithm periodically on the network according to the DRL-based time selection algorithm, a significant improvement in the total number of accepted slice requests can be achieved with only performing a limited number of re-configuration operations.

A user subscription model in mobile radio access networks with network slicing

Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g., radio access resources at each cell) are owned by the InP, and are shared by the NSTs to provide a service to their mobile users. In this context, we proposed a business model that includes resource allocation and user subscription to NSTs in a competitive setting, and provides, among other things, closed-form expressions for the subscription indicators in equilibrium of each NST at each cell. This model relies on the widely adopted logit model to characterize user subscriptions. However, as a consequence of user mobility and radio propagation, some of the underlying assumptions in the logit model do not hold. Therefore, further research is needed to assess the accuracy of the results provided by the logit model in a mobile radio scenario. We carry out a thorough evaluation of the validity of the model by comparing its results against those obtained through computer simulation. Our simulation model includes complete and realistic characterizations of user mobility and radio propagation. From the results, we conclude that in most cases the logit model provides valid results in a mobile radio scenario.

Strategic Resource Pricing and Allocation in a 5G Network Slicing Stackelberg Game

We consider a marketplace in the context of 5G network slicing, where Application Service Providers (ASP), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> slice tenants, providing heterogeneous services, are in competition for the access to the virtualized network resource owned by a Network Slice Provider (NSP), who relies on network slicing. We model the interactions between the end users (followers) and the ASPs (leaders) as a Stackelberg game. We prove that the competition between the ASPs results in a multi-resource Tullock rent-seeking game. To determine resource pricing and allocation, we devise two innovative market mechanisms. First, we assume that the ASPs are pre-assigned with fixed shares (budgets) of infrastructure, and rely on a trading post mechanism to allocate the resource. Under this mechanism, the ASPs can redistribute their budgets in bids and customise their allocations to maximize their profits. In case a single resource is considered, we prove that the ASPs’ coupled decision problems give rise to a unique Nash equilibrium. Second, when ASPs have no bound on their budget, we formulate the problem as a pricing game with coupling constraints capturing the shared resource finite capacities, and derive the market prices as the duals of the coupling constraints. In addition, we prove that the pricing game admits a unique variational equilibrium. We implement two online learning algorithms to compute solutions of the market mechanisms. A third fully distributed algorithm based on a proximal method is proposed to compute the Variational equilibrium solution of the pricing game. Finally, we run numerical simulations to analyse the market mechanism’s economic properties and the convergence rates of the algorithms.

Artificial Intelligence-Assisted Network Slicing: Network Assurance and Service Provisioning in 6G

6G networks are expected to provide instant global connectivity and enable the transition from “connected things” to “connected intelligence,” where promising network slicing can play an important role in network assurance and service provisioning for various demanding vertical application scenarios. On the basis of diversified massive data, artificial intelligence (AI)-assisted techniques are widely considered more suitable than traditional models and algorithms to deal with challenges faced by complex and dynamic slicing problems in 6G. In view of this, we provide a tutorial on AI-assisted 6G network slicing for network assurance and service provisioning, aiming to show the prospect of 6G slicing and the advantages of applying AI technology. Specifically, we propose six typical characteristics of 6G network slicing, analyze the feasibility of AI from different network domains and technical aspects, propose a case study on AI-assisted bandwidth scaling, and, finally, put forward the main challenges and open issues for its future development.

Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

The evolution of the future beyond-5G/6G networks toward a service-aware network is based on network slicing technology. With network slicing, communication service providers seek to meet all the requirements imposed by the verticals, including ultra-reliable low-latency communication (URLLC) services. In addition, the open radio access network (O-RAN) architecture paves the way for flexible sharing of network resources by introducing more programmability into the RAN. RAN slicing is an essential part of end-to-end network slicing since it ensures efficient sharing of communication and computation resources. However, due to the stringent requirements of URLLC services and the dynamics of the RAN environment, RAN slicing is challenging. In this article, we propose a two-level RAN slicing approach based on the O-RAN architecture to allocate the communication and computation RAN resources among URLLC end devices. For each RAN slicing level, we model the resource slicing problem as a single-agent Markov decision process and design a deep reinforcement learning algorithm to solve it. Simulation results demonstrate the efficiency of the proposed approach in meeting the desired quality of service requirements.

Intelligent and Collaborative Orchestration of Network Slices

5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.

Resource Allocation for Multi-Traffic in Cross-Modal Communications

Cross-modal communications that incorporate audio-visual and tactile signals will bring a more holistic immersive experience to people. However, due to the different transmission requirements of these signals, it is a challenging task to rationalize the allocation of transmission resources. Therefore, this work proposes a joint transmission scheme to deal with the resource allocation problem of diverse signals. Network slicing and puncturing architecture are introduced in the scheme to achieve flexible resource allocation and reduce the wasting of resources. To reduce the negative impact of puncturing transmission on users, we construct the optimization problem related to transmission rate and reliability. This problem can realize the reasonable allocation of radio resources and meet the transmission requirements of the two types of signals. Next, we divide the optimization problem into two parts: video traffic resources allocation and tactile traffic puncturing resources allocation. To solve both of the problems, we leverage the channel matching (CM) algorithm and puncturing resource allocation (PRA) algorithm. In addition, we discuss the advantages and disadvantages of both ways to occupy puncturing resources, namely, occupy resources proportionally (ORP) and occupy resources blocks for transmission (ORB). Finally, the effectiveness of the proposed scheme is verified by comparing the excepted rate and resources loss ratio of system users with different schemes.

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hierarchical architecture to manage network slices resources in a federated manner</i> . Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">specialized</i> clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Federated DRL</i> approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.

Resource Allocation in an Open RAN System Using Network Slicing

The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods.

5G Standalone Deployments Are on the Rise [Mobile Radio

One of the key decisions facing mobile operators worldwide is whether to launch 5G services in nonstandalone or standalone (SA) mode. The key difference is that the former relies upon existing 4G LTE network assets, such as an LTE evolved packet core, while the latter requires the deployment of a 5G core. Besides enabling new features, such as network slicing, 5G SA provides the full range of 5G capabilities in both public and private network deployment. This choice is not simply a matter of the availability of technology but also of spectrum, and we focus on the latter in the first reports of this “Mobile Radio” column.

Latency Equalization Policy of End-to-End Network Slicing Based on Reinforcement Learning

Network slicing can provide logically isolated networks on the shared network infrastructure by invoking multiple technologies and administrative domains to fulfill end-to-end (E2E) service level agreements (SLAs). To guarantee the E2E service communication quality in the sliced network, an SLA-based cross-domain orchestration framework is proposed in this paper. The framework includes an E2E cross-domain coordination orchestrator at the upper layer and multiple subordinate domain controllers. Furthermore, we design two latency equalization policies applied to the upper layer orchestrator to divide the latency budget for each lower layer domain. Based on the reinforcement learning approach, Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network SFC Mapping (PN-SFC), intra-domain resource allocation/mapping algorithms are designed independently for the lower radio access network (RAN) and core network (CN) domain controllers, respectively. The above algorithms are used to jointly optimize the enhanced mobile broadband (eMBB) users service satisfaction level and maximize the number of E2E accessed users. Simulation results show that our proposed algorithm can effectively guarantee the eMBB users QoS and improve the network capacity.

VTopology: Virtual MAC Address Aided Network Slicing in Multi-Tenant Data Centers

The shared nature of the network in Multi-Tenant Data Centers (MTDC) implies that network performance for tenants varies significantly. Efficient utilization of limited resources in MTDC is a challenge. Network virtualization is a promising technology for MTDC to provide customized and isolated networks to accommodate various tenants' demands. Driven by the massive increase of data traffic in MTDC, efficient and flexible network virtualization schemes are essential to guarantee seamless network resource sharing and complete isolation. This article presents a novel approach to customizing Virtual Topology (vTopology) for each tenant with self-control capability based on network slicing technology. The key technology behind this, a novel virtual MAC address (vMAC) is used to define network slices and act as a data link layer unique identifier in communications within vTopology virtual switches. Moreover, we propose Optimal Back-Forwarding Path (OBFP) to overcome interconnecting and data forwarding challenges between virtual switch ports allocated on different physical switches. With vTopology, we believe that the customization of virtual networks through network slicing technology enables MTDC operators to create more agile and flexible virtual networks and guarantee isolation and self-control for each tenant.

Blockchain and Game Theory Convergence for Network Slice Brokering

As a distributed ledger technology, blockchain has received significant attention in revolutionizing telecommunication and networking domains. This article proposes a blockchain-based network slice brokering mechanism for multioperator and multitenant environments of the envisioned 6G networks.

Learning-Empowered Resource Allocation for Air Slicing in UAV-Assisted Cellular V2X Communications

In this article, we propose a resource allocation scheme for air slicing in unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (V2X) communications. We consider a scenario, where multiple flexible UAVs are deployed as the aerial base station (BS) to assist terrestrial BS for providing service to vehicular users with the objective of maximizing the bandwidth efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Due to the uncertainty of the stochastic environment in the scenario, we formulate the optimization problem to be a stochastic game, which is an extension of game theory to Markov decision process-like environments for the case of multiple adaptive agents are involved to compete goals simultaneously. Nevertheless, the dynamic nature of both UAVs and vehicles pose the difficulty of perceiving and interacting with the unknown environment, the long short-term memory algorithm is used for extracting the features of the observation and making forecast on the mobility of UAVs and vehicles. Simulation results adduce the validity of the proposed scheme as compared with two benchmark schemes: Deep Q-network and deep deterministic policy gradient.

Stackelberg game-based dynamic resource trading for network slicing in 5G networks

Network slicing (NS) has been envisioned as the main technology in 5G to accommodate different virtualized operators with versatile service requirements, on a common substrate infrastructure. NS has come to radically reform the interaction among different entities in the resource sharing market, where resource is exchanged for monetary gains as a way of ensuring efficient resource utilization. However, resource exchange is only attractive to entities if they are able to achieve their respective benefits in a win–win situation. In this paper, we design an economic model that analyzes the pricing and purchasing interaction between multiple mobile virtual network operators (MVNOs) and their respective users for resource trading in NS. We formulate the pricing and purchasing problem as a two-stage multi-leader multi-follower (MLMF) Stackelberg game where the MVNOs as leaders set their unit price first, and then users as followers respond by determining their purchasing volume. Then, we theoretically prove the existence and uniqueness of a Nash equilibrium (NE). To obtain an optimal dynamic pricing solution for the MVNOs, we transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a method based on multi-agent dueling deep Q-Network (DQN) algorithm. Simulation results show that the proposed algorithm achieves convergence under competitive pricing scheme (CPS) and independent pricing scheme (IPS), while ensuring high MVNOs’ profit and users’ utility at acceptable levels. In terms of cumulative profits, the proposed dueling DQN-based pricing algorithm achieves percentage gains of 15.3%, 30.4%, and 71.4% against Q-learning-based pricing, premium pricing, and undercut pricing algorithms as benchmarks.

Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the ϵ-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively.

Network Slicing for IoT Ecosystem in 5G Networks

Network Slicing for IoT Ecosystem in 5G Networks

A Survey on Resource Management for Cloud Native Mobile Computing: Opportunities and Challenges

Fifth-generation mobile communication networks (5G)/Beyond 5G (B5G) can achieve higher data rates, more significant connectivity, and lower latency to provide various mobile computing service categories, of which enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low latency communications (URLLC) are the three extreme cases. A symmetrically balanced mechanism must be considered in advance to fit the different requirements of such a wide variety of service categories and ensure that the limited resource capacity has been properly allocated. Therefore, a new network service architecture with higher flexibility, dispatchability, and symmetrical adaptivity is demanded. The cloud native architecture that enables service providers to build and run scalable applications/services is highly favored in such a setting, while a symmetrical resource allocation is still preserved. The microservice function in the cloud native architecture can further accelerate the development of various services in a 5G/B5G mobile wireless network. In addition, each microservice part can handle a dedicated service, making overall network management easier. There have been many research and development efforts in the recent literature on topics pertinent to cloud native, such as containerized provisioning, network slicing, and automation. However, there are still some problems and challenges ahead to be addressed. Among them, optimizing resource management for the best performance is fundamentally crucial given the challenge that the resource distribution in the cloud native architecture may need more symmetry. Thus, this paper will survey cloud native mobile computing, focusing on resource management issues of network slicing and containerization.