A Comparative Study of Resource Allocation Algorithms Used in Network Virtualization Function

A new networking technology in the telecom sector called network function virtualization lowers operating and capital costs while enabling network service deployment. In NFV, seeks to tackle challenges by utilizing standardized IT virtualization technology to combine various types of network equipment onto industry-standard, high volume servers, switches and storage systems, which may be situated in data centers, network nodes, or at the end user’s location. The Virtual Network Functions (VNFs) worked as software oriented approach, it creates a high flexible and dynamic network to meet more several demand along with a series of research challenges, such as, VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with Network Function Virtualization Infrastructure (NFVI), among others. However, because network conditions and workloads are dynamic, effective resource management is still a major difficulty in NFV systems. This survey paper presents an overview of resource allocation algorithms in Network Functions Virtualization (NFV). We examine state-of-the-art approaches as Deep Reinforcement Learning (DRL), Parallel VNF Placement Framework (PVFP), RL Based Framework and Online Coordinated Resource Allocation (OCRA), analyzing speed, limitations, and suitability for dynamic environments. This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of Network Function Virtualization.