Memory Priority Scheduling Algorithm for Cloud Data Center Based on Machine Learning Dynamic Clustering Algorithm

Abstract

As the cloud data center (CDC) landscape continues to broaden, CDC resource utilization as the benchmark for assessing scheduling methodologies. Concurrently enhancing both CPU and memory utilization stands as a paramount priority. However, prevalent algorithms tend to solely prioritize CPU utilization while neglecting memory efficiency, ultimately escalating energy expenditure. This article initiates by conducting a comprehensive examination of memory utilization repercussions on CDCs. Subsequently, it facilitates the dynamic clustering of physical machines and virtual machine deployments, ensuring a balanced utilization profile. Furthermore, it introduces the memory priority scheduling algorithm for CDC based on machine learning dynamic clustering algorithm (PMPD). Comparative evaluations against other algorithms underscore the prowess of PMPD in concurrently optimizing CPU and memory utilization, thereby minimizing the number of active PMs and diminishing energy consumption of CDCs.