Managing energy-efficient virtual machines with QoS-awareness in cloud computing

Abstract

This paper proposes an improved genetic algorithm for dynamic resource management, taking into account network delay and energy consumption. The algorithm utilizes CloudSim and CloudAnalyst tools to analyze qualitative and quantitative its performance. The experimental results demonstrate that the algorithm reduces response time for user requests and improves Quality of Service (QoS) while consuming the same amount of power. Additionally, It also leads to lower power consumption for the same response time. This research finding is significant for enhancing the performance and efficiency of cloud computing platforms. The work holds practical value as it offers an effective solution for resource management in cloud computing environments. This study proposes an improved genetic algorithm that optimizes resource allocation, reduces network delay, improves energy efficiency, and enhances user experience in cloud computing technology. The algorithm is innovative and practical in solving dynamic resource management problems, providing valuable references for related research fields.