A Literature Survey on Resource Allocation for Network Function Virtualization With and Without Machine Learning in Cloud Computing

ABSTRACT The convergence of cloud computing, machine learning (ML), and network function virtualization (NFV) offers significant opportunities for advancing network infrastructure management by providing efficient, flexible, and scalable resource utilization. This study aims to provide a comprehensive review of the primary challenges and explores state‐of‐the‐art solutions in cloud computing for resource allocation (RA) specific to NFV environments. The paper highlights the importance of adopting multifaceted strategies to optimize RA and enhance the efficiency, and adaptability of cloud systems that handle RA without ML, and with ML in NFV settings. In addition, gap identification is also discussed, emphasizing many needs: (1) the need for extending the NFV RA in the case of wireless networks; (2) the need for enhanced security protocols to fully harness the potential of ML within resource function virtualization (RFV) environments, ensuring that network infrastructures are not only efficient but also resilient and secure; and (3) the need to develop more efficient ML‐based RA for NFV, considering the trade‐off between performance and accuracy.