Caching Resource Sharing for Network Slicing in 5G Core Network

Network slicing has been considered a promising technology in next generation mobile networks (5G), which can create virtual networks and provide customized service on demand. Most existing works on network slicing mainly focus on virtualization technology, and have not considered in-network caching well. However, in-network caching, as the one of the key technologies for information-centric networking (ICN), has been considered as a significant approach in 5G network to cope with the traffic explosion and network challenges. In this article, the authors jointly consider in-network caching combining with network slicing. They propose an efficient caching resource sharing scheme for network slicing in 5G core network, aiming at solving the problem of how to efficiently share the limited physical caching resource of Infrastructure Provider (InP) among multiple network slices. In addition, from the perspective of network slicing, the authors formulate caching resource sharing problem as a non-cooperative game, and propose an iteration algorithm based on caching resource updating to obtain the Nash Equilibrium solution. Simulation results show that the proposed algorithm has good convergence performance, and illustrate the effectiveness of the proposed scheme.

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Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing

Sensors ◽

10.3390/s19143107 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3107 ◽

Cited By ~ 6

Author(s):

Ramon Sanchez-Iborra ◽

José Santa ◽

Jorge Gallego-Madrid ◽

Stefan Covaci ◽

Antonio Skarmeta

Keyword(s):

Intelligent Transportation Systems ◽

Transportation Systems ◽

The Internet ◽

Critical Systems ◽

Core Network ◽

Resource Assignment ◽

Network Slicing ◽

Internet Of Vehicles ◽

5G Network ◽

Multi Access

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

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Small-Scale 5G Testbeds for Network Slicing Deployment: A Systematic Review

Wireless Communications and Mobile Computing ◽

10.1155/2021/6655216 ◽

2021 ◽

Vol 2021 ◽

pp. 1-26

Author(s):

Ali Esmaeily ◽

Katina Kralevska

Keyword(s):

Mobile Networks ◽

Small Scale ◽

Design Criteria ◽

Core Network ◽

Summary Table ◽

Network Slicing ◽

Fifth Generation ◽

Software Packages ◽

Functional Blocks ◽

Cost Efficient

Developing specialized cloud-based and open-source testbeds is a practical approach to investigate network slicing functionalities in the fifth-generation (5G) mobile networks. This paper provides a comprehensive review of most of the existing cost-efficient and small-scale testbeds that partially or fully deploy network slicing. First, we present relevant software packages for the three main functional blocks of the ETSI NFV MANO framework and for emulating the access and core network domains. Second, we define primary and secondary design criteria for deploying network slicing testbeds. These design criteria are later used for comparison between the testbeds. Third, we present the state-of-the-art testbeds, including their design objectives, key technologies, network slicing deployment, and experiments. Next, we evaluate the testbeds according to the defined design criteria and present an in-depth summary table. This assessment concludes with the superiority of some of them over the rest and the most dominant software packages satisfying the ETSI NFV MANO framework. Finally, challenges, potential solutions, and future works of network slicing testbeds are discussed.

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Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

Applied Sciences ◽

10.3390/app9204361 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4361

Author(s):

Xin Li ◽

Chengcheng Guo ◽

Jun Xu ◽

Lav Gupta ◽

Raj Jain

Keyword(s):

Cost Ratio ◽

Core Network ◽

Bandwidth Utilization ◽

Network Resource ◽

Network Slicing ◽

Acceptance Ratio ◽

Second Stage ◽

Node Ranking ◽

And Topology ◽

5G Network

Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.

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Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning

10.36227/techrxiv.14498190.v1 ◽

2021 ◽

Author(s):

William Fernando Villota Jácome ◽

Oscar Mauricio Caicedo Rendon ◽

Nelson Luis Saldanha da Fonseca

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Admission Control ◽

Time Windows ◽

Core Network ◽

Network Slicing ◽

Q Learning ◽

Model Free ◽

5G Network ◽

Substrate Network

Network Slicing is a promising technology for providing customized logical and virtualized networks for the industry’s vertical segments.This paper proposes SARA and DSARA for the performance of admission control and resource allocation for network slice requests of eMBB, URLLC, and MIoT type in the 5G core network. SARA introduced a Q-learning based algorithm and DSARA a DQN-based algorithm to select the most profitable requests from a set that arrived in given time windows. These algorithms are model-free, meaning they do not make assumptions about the substrate network as do optimization based approaches.

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Principles and Enabling Technologies of 5G Network Slicing

Paving the Way for 5G Through the Convergence of Wireless Systems - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-5225-7570-2.ch011 ◽

2019 ◽

pp. 271-284

Author(s):

Zoran Bojkovic ◽

Bojan Bakmaz ◽

Miodrag Bakmaz

Keyword(s):

Operating Cost ◽

Mobile Systems ◽

Core Network ◽

Network Slicing ◽

Dynamic Slicing ◽

Enabling Technologies ◽

Radio Access ◽

5G Network ◽

Comprehensive Survey ◽

Multiple Services

5G mobile systems can be comprehended as highly flexible and programmable E2E networking infrastructures that provide increased performance in terms of capacity, latency, reliability, and energy efficiency while meeting a plethora of diverse requirements from multiple services. Network slicing is emerging as a prospective paradigm to meet these requirements with reduced operating cost and improved time and space functionality. A network slice is the way to provide better resource isolation and increased statistical multiplexing. With dynamic slicing, 5G will operate on flexible zone of the network, permitting varying, adaptable levels or bandwidth and reliability. In this chapter, a comprehensive survey of network slicing is presented from an E2E perspective, detailing its origination and current standardization efforts, principal concepts, enabling technologies, as well as applicable solutions. In particular, it provides specific slicing solutions for each part of the 5G systems, encompassing orchestration and management in the radio access and the core network domains.

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Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning

10.36227/techrxiv.14498190 ◽

2021 ◽

Author(s):

William Fernando Villota Jácome ◽

Oscar Mauricio Caicedo Rendon ◽

Nelson Luis Saldanha da Fonseca

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Admission Control ◽

Time Windows ◽

Core Network ◽

Network Slicing ◽

Q Learning ◽

Model Free ◽

5G Network ◽

Substrate Network

Network Slicing is a promising technology for providing customized logical and virtualized networks for the industry’s vertical segments.This paper proposes SARA and DSARA for the performance of admission control and resource allocation for network slice requests of eMBB, URLLC, and MIoT type in the 5G core network. SARA introduced a Q-learning based algorithm and DSARA a DQN-based algorithm to select the most profitable requests from a set that arrived in given time windows. These algorithms are model-free, meaning they do not make assumptions about the substrate network as do optimization based approaches.

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SDN-Based Network Slicing Mechanism for a Scalable 4G/5G Core Network: A Kubernetes Approach

Sensors ◽

10.3390/s21113773 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3773

Author(s):

Robert Botez ◽

Jose Costa-Requena ◽

Iustin-Alexandru Ivanciu ◽

Vlad Strautiu ◽

Virgil Dobrota

Keyword(s):

Web Services ◽

Mobile Network ◽

High Availability ◽

Core Network ◽

Network Resources ◽

Network Slicing ◽

5G Network ◽

Amazon Web Services ◽

Network Functions ◽

End To End

Managing the large volumes of IoT and M2M traffic requires the evaluation of the scalability and reliability for all the components in the end-to-end system. This includes connectivity, mobile network functions, and application or services receiving and processing the data from end devices. Firstly, this paper discusses the design of a containerized IoT and M2M application and the mechanisms for delivering automated scalability and high availability when deploying it in: (1) the edge using balenaCloud; (2) the Amazon Web Services cloud with EC2 instances; and (3) the dedicated Amazon Web Services IoT service. The experiments showed that there are no significant differences between edge and cloud deployments regarding resource consumption. Secondly, the solutions for scaling the 4G/5G network functions and mobile backhaul that provide the connectivity between devices and IoT/M2M applications are analyzed. In this case, the scalability and high availability of the 4G/5G components are provided by Kubernetes. The experiments showed that our proposed scaling algorithm for network slicing managed with SDN guarantees the necessary radio and network resources for end-to-end high availability.

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