VNF and CNF Placement in 5G: Recent Advances and Future Trends

Abstract

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network's performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include microservices, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

“…FL is also examined, as it is a distributed approach orthogonal to RL and can be combined with it, resulting in federated RL (FRL). For other types of approach, the reader may consult, e.g., [4,[7][8][9]31,32] (Table A2 in Appendix A provides some further context about each of these references). Briefly, distributed RL methodologies introduce multiple agents to manage the network resources, with each one performing actions on the network to maximize a long-term reward in a cooperative or competitive fashion.…”

“…As mentioned earlier, computational resources will be vital for future applications, and these resources are expected to be available both at the edge and core of the network [1,3,9]. VNFs and containerized network functions (CNFs) will provide processing capacity to users.…”

“…At the same time, VNFs need to be placed at the appropriate location in order to serve incoming tasks. Works covering various aspects of multi-access edge computing (MEC) planning, management, orchestration, and optimization can be found in [9,47].…”

“…Exploiting AI for network management in general and resources management (RM) in particular is not a 6G novelty, as research in this direction has already made headway in the context of 5G networks; see, e.g., [4,[7][8][9] and references therein. Relevant examples include AI-based solutions for spectrum management, power allocation, user association, beamforming, mobility management, cache management, virtual network function (VNF) placement, and end-to-end RM and orchestration.…”

Research on Service Function Chain Embedding and Migration Algorithm for UAV IoT

“…Network virtualization is a technology that implements multiple virtual networks on the same physical network to improve the utilization, flexibility, and innovation of network resources [4]. Network virtualization enables the software and programmability of network functions, reduces network costs and complexity, and improves the delivery speed and quality of network services.…”