A model for controlling virtualized network functions under dynamic load changes

DOI: 10.31673/2412-9070.2025.025489

Abstract

The paper addresses the challenge of improving the management efficiency of virtualized network functions (NFV) in software-defined network (SDN) environments under dynamic load changes and varying Quality of Service (QoS) requirements. The increasing scale and complexity of network structures necessitate the introduction of intelligent automation mechanisms for network infrastructure management.
A conceptual model of intelligent NFV is proposed, integrating fuzzy production-rule inference ("if-then" rules) into the orchestration process of virtual network functions. This model enables real-time monitoring of network metrics and resource states to automatically make decisions regarding scaling, resource allocation, and routing, thereby adapting network operations to current conditions.
It is argued that traditional approaches with rigid threshold settings or static policies cannot provide sufficient flexibility and responsiveness in modern networks. In contrast, the use of fuzzy logic allows for processing imprecise input data, incorporating expert knowledge, and achieving smooth adaptive management without abrupt transitions. The paper outlines the structure of the proposed model, describes its operating algorithm, and provides examples of fuzzy rules.
The application of this approach demonstrates more efficient use of data center resources, main tains stable latency and other QoS metrics even under sudden traffic changes, and reduces the risk of network overload. This is particularly important for critical services requiring continuous operation and low latency (real-time services such as VoIP, video streaming, IoT, etc.).

Keywords: software-defined networks; network function virtualization; fuzzy production-rule inference; orchestration; quality of service (QoS); intelligent control system.