AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments

Abstract

The accurate calculation and comparison of performance in cloud environments are critical for optimizing resource utilization, particularly with the increasing use of virtual machines (VMs) and containers. This research proposes an AI-driven resource management framework that surpasses traditional machine learning algorithms by enabling real-time, autonomous performance optimization. While machine learning models provide predictive capabilities, they often require manual tuning and retraining for changing workloads. In contrast, the proposed AI-driven system, utilizing techniques such as reinforcement learning and adaptive optimization, continuously adjusts resource allocation based on real-time performance metrics like response time, throughput, and server utilization. This dynamic, self-improving system can respond to fluctuating workloads and network conditions without the need for constant retraining, offering superior flexibility and faster response times. The framework will be validated through extensive experiments across multi-cloud and edge computing environments, demonstrating its ability to significantly reduce calculation time while improving scalability and efficiency. Additionally, this approach incorporates enhanced security mechanisms, combining the isolation benefits of VMs with the lightweight efficiency of containers, providing a comprehensive, real-time solution for cloud-native applications.