Network Slicing: History
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Contributor:
Ralph Voltaire J. Dayot
,
In-Ho Ra
,
Hyung-Jin Kim

5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. 

  • 5G
  • network slicing

1. Introduction

In recent years, the fifth generation of communication networks, or simply 5G, has been continuously reshaping the ICT landscape. Along with the advent of 5G and beyond technologies, many services have also emerged, including augmented and virtual reality, vehicle-to-everything communications, e-health, and smart homes. Due to this diversity of existing services, the International Telecommunication Union (ITU) has identified three major usage scenarios for 5G services, namely: Ultra-Reliable Low Latency Communications (uRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communications (mMTC) [1]. Each of these usage scenarios has specific requirements that distinguish the types of resources allocated for each service request. To be specific, adaptive and on-demand resource provisioning methods based on varying service request types are needed to meet user needs [2]. Moreover, it is essential to meet the various requirements of these services to provide end-users with the best quality of service (QoS) possible.
One of the critical technologies on 5G and beyond systems is network slicing (NS) [3]. NS refers to creating multiple virtual networks within a 5G physical infrastructure to constitute a physical network. This technology is made possible by Software-Defined Networking (SDN) [4] and Network Function Virtualization (NFV) [5]. Moreover, each slice in the network has certain network functions tailored to the different services required by users. A classification of these slices includes service, resource, and deployment-driven NS solutions [6]. Moreover, creating slices for specific services within the physical network also ensures that network resources are efficiently utilized or allocated throughout the system. Likewise, NScan be broken down into three major processes: slice creation, slice isolation, and slice management [7]. This categorization of NS processes is further expanded into slice monitoring, slice mapping, and slice provisioning, as discussed in [8].
Recently, NS has become the subject of most research related to 5G networks, as it can improve service delivery and QoS. For example, the work in [9] studied the management and allocation of radio access networks (RAN) resources focusing on its impact on uRLLC and eMBB slices. The authors proposed an intelligent decision-making technique to manage network traffic and allocate the required resources. The work in [10] enumerates several factors that affect the implementation of network slices. These include resource allocation, slice isolation, security, RAN virtualization, feature granularity, and end-to-end (E2E) slice orchestration. Sohaib et al. presented the applications of machine learning (ML) and artificial intelligence (AI) for NS solutions in [11]. Their paper listed various ML and AI algorithms, and applications for different NS use-cases such as mobility prediction and resource management. Reference [12] studied the challenges in slice admission and management. The work investigated network revenue, QoS, inter-slice congestion, and slice fairness and possible solutions through various slice admission strategies and optimization techniques. Ye et al. [13] investigated how the NS process can improve E2E QoS in 5G networks by proposing an NS framework through effective resource allocation. Two scenarios, including (1) radio resource slicing for heterogeneous networks (HetNets) and (2) bi-resource slicing for core networks, were investigated to evaluate the efficiency of the proposed framework.
Recent studies have also applied the growing popularity of Deep Learning techniques to NS scenarios. For example, in [14], a framework for NS called DeepSlice was developed to classify incoming network service requests as either uRLLC, eMBB, or mMTC requests using Deep Learning. Ideal network slices for slice requests are then provided based on the classification results, with additional efficient resource allocation and network traffic regulation. The same authors of DeepSlice have also presented Secure5G [15], a Deep Learning-based framework designed for secure E2E-NS. This framework uses Deep Learning to identify network security threats through an NS- as-a-service model. Furthermore, Abbas et al. [16] proposed an intent-based NS (IBNSlicing) framework that aims to manage RAN and core network resources by using Deep Learning effectively. The framework focuses more on the process of slice orchestration, with the primary goal of improving the data transfer rate.

2. Resource Allocation in 5G Networks

The efficient allocation of network resources is critical in mobile communication networks. Since the network receives many requests at a given time, it should be able to handle these requests without compromising the provided QoS. Therefore, significant efforts are being put into developing techniques for optimal resource allocation through NS. The authors in [17] presented various resource allocation methods for different 5G network scenarios. These include optimization, game theory, auction theory, and machine learning methods. The study in [18] suggests that user assignment, network utility, and throughput play an essential role in formulating resource allocation methods. Furthermore, in [19], the authors emphasized optimal dynamic resource management and aggregation as crucial components for effective resource allocation in 5G networks.

3. Network Slicing Solutions in 5G Networks

This section discusses the existing works focused on implementing NS solutions. Guan et al. in [20] utilized complex network theory methods to develop a service-oriented deployment approach to E2E-NS. In their work, NSRs are provisioned with slices by selecting the network node with the most favorable node importance among all candidate nodes at each time step. The importance of the node is calculated based on its degree and centrality. In addition, the authors then perform shortest path estimation to map all the selected nodes and create a network slice. Moreover, different slice provisioning strategies have also been formulated for uRLLC, eMBB, and mMTC service requests. Sciancalepore et al. designed a network slice brokering agent named ONETS [21]. The primary goal of their work is the development of a slice admission framework based on the multi-armed bandit problem. Such work has shown an increase in service request acceptance and maximization of network multiplexing gains through rewards systems, outperforming conventional existing reinforcement learning algorithms in the process. In [22], Abidi et al. attempted to address a slice allocation problem brought about by a massive data influx in the network. They introduced a Glowworm Swarm-based Deer Hunting Optimization Algorithm (GS-DHOA) to perform slicing classification. Based on their results, the proposed method distinguished uRLLC, eMBB, and mMTC requests and provided the necessary slices with great accuracy compared to other candidate algorithms utilized in the simulations. The work in [23] proposed a NS strategy by implementing a multi-criteria decision-making (MCDM) method. The authors used a slice provisioning algorithm based on the VIKOR approach to solve a 5G core-network-slice-provisioning problem (5G-CNSP). They also used complex network theory and a two-stage heuristic approach. The MCDM process for node selection considered the computational capacity and bandwidth of the nodes, as well as the local and global node topologies as decision parameters. Based on their results, the authors concluded that the proposed slicing strategy provides high RE and acceptance rate in high network traffic while addressing network security requirements. Fossati et al. [24] proposed a multi-resource allocation protocol for resource distribution on multiple network tenants. Their work proposed an optimization framework named multi-resources network slicing or MURANES that is based on the ordered weighted average (OWA) operator. The proposed solution considers the congestion and demand for resources from tenants. Likewise, the authors formulated allocation rules for single and multi-resource allocation scenarios. Results indicated that the proposed framework could address the multi-resource allocation problem while considering traffic support and heterogeneous congestion. The work in [25] introduced a prediction-assisted adaptive network slice expansion algorithm utilizing the Holt–Winters (HW) prediction method. The authors aimed to predict various changes occurring within the network and then provide services through a VNF adaptive scaling strategy. Network slices were proactively deployed based on NSRs received using network traffic rate and available resource information as the main parameters. Moreover, implementing the proposed approach resulted in lower energy consumption rates and slice deployment costs within the network. Sun et al. in [25] developed a RAN slicing framework for 5G networks aimed at maximizing bandwidth utilization and ensuring network QoS at the same time. Their paper focused on slice admission for NSRs through a joint slice association and bandwidth allocation approach. Slice admission policies for QoS optimization and user admissibility were then formulated, which increased the number of served users with the improved bandwidth consumption. Li et al. in [26] presented an application of Deep Q-Learning with the E2E-NS. The proposed algorithm maximizes user access through RAN and core network slices through dynamic resource allocation. Results of the simulations showed that the proposed reinforcement learning approach yielded a higher number of user access in both delays constrained and rate constrained slices. A summary of the related works is provided in Table 1.
Table 1. A Summary of Related Work on Resource Allocation and Network Slicing.
Ref Objective Proposed Solution Performance Metrics
[14] Optimal slice selection and prediction for mobile devices and adaptive slice assignments in the case of network failures A Deep Learning and machine learning-based network slicing scheme that analyzes and predicts network traffic patterns for optimal resource allocation
  • Slice prediction accuracy
  • Slice utilization
  • Slice Failure
[15] Secure network slicing for user equipment access A neural network-based network slicing model for proactive threat detection and elimination
  • Threat detection accuracy
[16] Efficient slice management and resource allocation for RAN and CN An intent-based network slicing framework for upper-level slice configuration and orchestration
  • Data rate
  • Network throughput
[19] Integration of Mobile Edge Computing (MEC) for efficient allocation of idle 5G mobile network resources in urban settings A MEC-based 5G network resource allocation framework for aggregated idle network resources
  • Service rate (in terms of served and blocked requests)
[20] Service-oriented network E2E slice mapping and deployment A complex network theory-based slice mapping and creation with slice deployment policy formulation for eMBB, mMTC, and uRLLC use-cases
  • Resource efficiency
  • Acceptance Ratio
[21] Efficient online service request-to-network slice brokering while considering network resource availability A multi-armed bandit-based slice brokering method for budgeted resource lock-up for 5G network tenants
  • Cumulative agent rewards
  • Tenant selection ratio
  • System utilization distribution
[22] Efficient network slicing using machine learning and AI techniques A Deep Belief Network and Neural Network-based network slice classification scheme with Glowworm Swarm-based parameter weight optimization
  • Learning percentage
  • Accuracy
  • Sensitivity
  • Specificity
  • Precision
[23] Implementation of MCDM-based node ranking for effective slice provisioning A VIKOR algorithm-based core-network-slice-provisioning approach to secure network slicing
  • Acceptance ratio
  • Revenue-to-cost ratio
  • Node utilization
[24] multi-resource allocation while considering resource usage fairness and system efficiency A multi-resource allocation framework based on the Ordered Weighted Average (OWA) operator for resource availability and user demand information aggregation
  • Resource utilization
  • Fairness
[25] Efficient slice deployment through cost and network energy reduction A dynamic slice deployment through a prediction-assisted adaptive network slice algorithm using Holt–Winters (HW) prediction
  • Energy consumption
  • Link utilization
  • Total slicing cost
[26] Service provisioning in RAN slices while ensuring QoS and optimal resource utilization A unified RAN slice provisioning framework for maximization of bandwidth utilization with user QoS guarantee
  • UE admission rate
  • Bandwidth consumption
  • Algorithm running time11
[27] RAN and CN resource allocation through E2E NS while considering access rate and delay service requirements A proposed Deep Q-Network algorithm for E2E wireless resource allocation and service link mapping on 5G network slices
  • E2E Access rate

This entry is adapted from the peer-reviewed paper 10.3390/network2030023

References

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