Virtualisation and network management: Best practices for improving efficiency

Abstract

This study aimed to identify optimal approaches for enhancing the efficiency of network management and virtualisation processes. Four key methods were examined: resource allocation optimisation using intelligent algorithms, load forecasting through Machine Learning models, dynamic load balancing enabled by software-defined networking technologies, and automated resource management guided by policy-based frameworks. The research provided a detailed analysis of each method, including their operating principles and implementation stages. Diagrams illustrating the architecture and operational mechanisms of these methods were presented, alongside practical examples of their application in various infrastructures, such as cloud environments, software-defined networks, and corporate data centres. Additionally, software implementations in Python were developed, demonstrating the functionality of the proposed approaches. The findings highlighted several key benefits: resource allocation optimisation effectively improved the utilisation of computing power in cloud environments; load forecasting enabled proactive infrastructure adaptation to peak activity periods; SDN-based load balancing facilitated centralised traffic management and reduced latency, which is critical for modern corporate networks; and automated resource management through policies reduced costs and supported system stability by dynamically responding to load variations. A comparative analysis of the methods revealed distinct advantages and limitations for each approach, emphasising the importance of selecting the appropriate method based on the specific requirements of the infrastructure. Overall, the results confirmed the viability of these approaches for enhancing the performance and stability of virtualised environments and network systems.