Double level Code Scanning Leveraging network virtualization to decrease risks of memory starvation and insecure programming

Abstract

This study investigates the impact of a software-defined networking (SDN) controller on resource utilization, system stability, and performance in cloud environments, with a specific focus on the deployment of machine learning techniques. The SDN controller utilizes machine learning algorithms to dynamically detect and block vulnerable and memory-intensive cloudlet codes. The results demonstrate that the deployment of machine learning in the SDN controller significantly improves resource allocation, mitigates resource contention, and reduces the risk of memory exhaustion. By accurately identifying and preventing the execution of insecure cloudlets, the machine learning-enabled SDN controller enhances system security and maintains optimal resource utilization. Moreover, the study highlights the positive influence of the SDN controller on system stability, preventing disruptive operations and minimizing the likelihood of system crashes or slowdowns.