| Literature DB >> 36081079 |
Sumbal Zahoor1, Ishtiaq Ahmad1, Mohamed Tahar Ben Othman2, Ali Mamoon3, Ateeq Ur Rehman4, Muhammad Shafiq5, Habib Hamam6,7,8,9.
Abstract
Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.Entities:
Keywords: AI; CSPs; ML; NS projects; automation; code flow; network slicing; orchestration
Mesh:
Year: 2022 PMID: 36081079 PMCID: PMC9459685 DOI: 10.3390/s22176623
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.847
Figure 1Evolution of NGWNs (multiple services sharing one link to 4G framework for dedicated core selection based on user equipment (UE)).
Figure 2The 5G framework for NS based on SST.
Standardized SST values and their features.
| SST | SST Value | Expected Features |
|---|---|---|
| eMBB | 1 | Extreme throughput |
| uRLLC | 2 | High reliability |
| mIoT | 3 | Higher linking density |
Summary of major surveys on NS and their objectives.
| Studies | Applications | Classes | Business View | Orchestration | Cloud-Native | Functional | AI and ML | Challenges | Projects |
|---|---|---|---|---|---|---|---|---|---|
| [ | ✓ | ✕ | ✕ | ✓ | ✕ | ✕ | ✕ | ✓ | ✕ |
| [ | ✓ | ✕ | Limited | ✓ | ✓ | ✓ | ✕ | ✓ | ✕ |
| [ | ✓ | ✕ | ✕ | ✓ | ✕ | ✕ | ✕ | ✓ | ✕ |
| [ | ✓ | ✕ | Limited | ✓ | ✕ | ✕ | Limited | ✓ | ✕ |
| [ | ✓ | ✓ | Limited | ✕ | Limited | ✕ | ✓ | ✓ | ✓ |
| Our Work | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Summary of acronyms.
| Acronyms | Full Form | Acronyms | Full Form | Acronyms | Full Form |
|---|---|---|---|---|---|
| NS | Network Slicing | uRLLC | Ultra-Reliable Low Latency Communication | VM | Virtual Machine |
| CSPs | Communication Service Providers | SST | Slice/Service Type | SBA | Services Based Architecture |
| AI | Artificial Intelligence | E2E | End-to-End | NFs | Network Functions |
| ML | Machine Learning | CAGR | Compound Annual Growth Rate | UE | User Equipment |
| NGWN | Next Generation Wireless Network | MNOs | Mobile Network Operators | NRF | Network Resource Function |
| MEC | Mobile Edge Computing | NSaaS | Network Slice as a Service | UDM | Unified Data Management |
| IoT | Internet of Things | CN | Core Network | AF | Application Function |
| 3GPP | Third Generation Partnership Project | TN | Transport Network | AMF | Access and Mobility Management Function |
| SDO | Standard Developing Organization | RAN | Radio Access Network | SMF | Session Management Function |
| QoS | Quality of Service | CSI | Communication Service Instance | PDU | Protocol data unit |
| AR | Augmented Reality | NSSI | Network Slice Subnets Instances | UPF | User Plane Function |
| APN | Access Point Name | CSMF | Communication Service Management Function | AUSF | Authentication Server Function |
| EUTRAN | Evolved UMTS Terrestrial Radio Access Network | NSMF | Network Slice Management Function | BSF | Binding Support Function |
| EPC | Evolved Packet Core | NSSMF | Network Slice Subnet Management Function | PCF | Policy Control Function |
| DCN | Dedicated Core Network | NFMF | NF Management Function | DHCP | Dynamic Host Configuration Protocol |
| SDN | Software Defined Networking | VNF | Virtual Network Function | PDN | Packet Data Network |
| NFV | Network Function Virtualization | PNF | Physical Network Function | DN | Data Network |
| RAN | Radio Access Network | MANO | Management and Orchestration | CM | Configuration Management |
| SLA | Service Level Agreement | NFVO | NFV Orchestrator | FM | Fault Management |
| GSMA | Global System for Mobile Communications | NFVIs | NFV Infrastructure | ISG ENI | Industry Specification Group on Experiential Networked Intelligence |
| NSI | Network Slice Instance | LCM | Life Cycle Management | BPM | Business Process Management |
| ITU | International Telecommunication Union | VIM | Virtualized Infrastructure Manager | DRL | Deep Reinforcement Learning |
| eMBB | Enhanced Mobile Broadband | CNCF | Cloud-Native Core Network | SVM | Support Vector Machine |
Business requirements.
| Performance | Operational | Functional | |
|---|---|---|---|
|
| Throughput | Design and extensive process capacity | Isolation and flexibility |
| Latency | SLAs and active preservation | Placement and delay tolerance | |
| Synchronization | Supervision potential | Security |
Figure 3The view of end-user NS market.
Figure 4Organization of NS lifecycle.
Figure 5Fifth-generation orchestration with ETSI NFV.
Figure 6The 5G network core service-based architecture (SBA).
Figure 7Registration logic of 5G SBA.
Figure 8The 5G network SBA PDU session establishment.
Summary of 5G NS initiatives.
| Project Name | Application | Tools | Features | Objectives |
|---|---|---|---|---|
| 5G-XHAUL | Automotive, | ✓,✓ | NS concept and administration | Create a robust SDN control plane and request statistical models that are agility smart for optical/wireless 5G networks. |
| 5G!PAGODA | IoT, human | ✓,✓ | Coherent architecture | The primary goals are to create a consistent infrastructure that allows Europe and Japan to collaborate on research and standards. The suggested innovations are designed to work with a common SDN/NFV-based architecture. |
| 5G-MoNArch | Smart cities, | ✓,✓ | Software development and validation kits | Developed a detailed NS framework and used its flexibility to fully integrate functionalities necessary for industrial, media and entertainment, and smart city use cases. |
| ONE5G | Agricultural, | ✓,✓ | NS design and | To suggest enhanced network skills and modifications ahead of release fifteen to allow multi-service function and functional execution of “5G advanced (pro),” including upcoming network applications, advanced massive MIMO enablers, and link control. |
| SLICENET | Smart cities, | ✓,✓ | Software development and support | Generate a platform for smart network control, governance, and orchestration in SDN/NFV-enabled 5G networks to support infrastructure exchange across multiple operator domains. |
| 5G-TANGO | Broadcasting, | ✓,✓ | _____ | Provides commercial prospects through network adaptation and adaptation to vertical technical standards by decreasing the access barrier for third-party designers and enabling the building and integration of virtual network functions (VNFs) and application elements as “network services”. |
| MATILDA | Media, smart cities, automotive, industry 4.0 | ✓,✓ | _____ | Design a fundamental shift in the development of software for 5G-ready solutions, as well as virtual and physical network operations and network services. A cross virtualized infrastructure manager helps to manage cloud/edge computing and IoT resources from various locations. |
| 5GCity | Smart cities, neutral masses, broadcasting | ✓,✓ | _____ | Optimize the financial return for the whole virtual market chain and to deploy a common, multi-tenant, open forum that expands the (consolidated) cloud model to the network’s outer limit. |
| 5G ESSENCE | Entertainment, | ✓,✓ | _____ | Manages the concepts of small cell as a service and edge cloud technology via enabling the drivers and reducing obstacles in the small cell industry, which anticipated to expand quickly and play a key role in the 5G ecosystem. |
| 5G-TRANSFORMER | E-health, | ✓,✓ | NS design and organization | Create a 5G network architecture centered on SDN/NFV that tailored to certain vertical sectors. |
| 5GMobix | Associated and autonomous driving | ✕,✕ | Automated vehicle functionalities | Intends to link the benefits of 5G technology with sophisticated connected autonomous mobility applications to allow novel, traditionally implausible, autonomous car applications, both technically and commercially. |
| Primo-5G | Smart firefighting | ✕,✕ | Network framework | Demonstrate a comprehensive 5G system capable of providing interactive virtual solutions for moving items, achieved with cross-continental testbeds that connect radio access and core networks built by different project participants. |
| 5G DRONES | eMBB, mIoT, | ✕,✕ | Innovative developments | The drones intended to assess various UAV use-cases for eMBB, uRLLC, and mIoT 5G services as well as validate 5G KPIs for supporting them. The project will build on the ICT-17 projects’ 5G infrastructure and number of support locations while also identifying and developing the remaining elements. |
| INSPIRE-5Gplus | Self-directed and connected vehicles to critical industry 4.0 | ✕,✓ | System framework, protection, and isolation | Intends to bring a significant shift in the access control of 5G networks and well beyond at the platform, vertical application, and quality of service. |
| AutoAir | Authentication and advancement of associated and independent automobiles | ✕,✕ | System framework | Allow the testing and deployment of self—driving technology. In addition to requiring more network bandwidth than is currently available, fast travel speeds hinder cell-tower handoff. It will also look at whether these 5G connection options may be applied to both road and rail transportation. |
| MonB5G | Zero-touch processing and planning across business zones | ✕,✕ | E2E orchestration and protection | Allow NS at enormous sizes for 5G LTE and beyond, offer zero-touch administration and orchestration. |
| Semantic | Multi-GHz range networks, MEC-enabled use provisioning and E2E | ✕,✕ | E2E orchestration | Presents a unique research training system for multi-Ghz limit connectivity, MEC enabled approach encompasses, and E2E NS, all integrated and jointly managed with forward data service automatic control that powers the large amounts of portable BIG DATA triggered into the cellular connection. |
| Hexa-X | Sustainable growth, huge linking, tele-presence, and regional trust areas | ✕,✓ | System framework, scalability, protection, and orchestration | Aims to create leading technology enablers in the following areas: inherently new radio access techniques at high frequencies and resolution segmentation and sensor-based; integrated smartness via AI-driven radio interface and management for large scale deployments; 6G structural enablers for system partitioning and flexible reliability. |
The perspective of SDOs and industrial forums on using AI and ML.
| Projects | Objectives |
|---|---|
| ETSI ISG ENI [ | The Industry Specification Group on Experiential Networked Intelligence (ISG ENI) manages establishing policies that use AI mechanisms to improve the operator practical experience by identifying and combining evolving knowledge, allowing operators to make more prompt decisions, and aiding in network management and orchestration. |
| ITU FG-ML5G [ | The goal of the ML5G Focus Group was to undertake an ML evaluation for future networks and to highlight significant gaps and concerns in standardized processes associated with this topic. In addition, technical elements such as use cases, requirements, and architectures are examined. The Focus Group operated as an open venue for specialists from ITU members and non-members to go forward with ML research linked to future networks, including 5G. |
| ISO/IEC JTC 1/SC42 [ | Establish a set of standards for determining the context, resources, and processes for creating and deploying AI applications. It can be used by ISO, IEC, and JTC1 technical committees and subcommittees to expand on this work in developing standards for AI applications in their respective areas of interest. The recommendations give a high-level overview of the AI application environment, stakeholders and their responsibilities, the system’s life cycle, and common AI application features. |
| TM Forum Smart BPM [ | SMART business process management (BPM) enables digital transformation catalyst who has previously proved the benefits of automated operations. This was accomplished through a business “process mining” and the instruments of adaptive discovery and orchestration of workflows. The use of analytics and big data also provided insights that enabled to leverage of user experience and network optimization. |