Intelligent Resource Optimization in Cloud Systems Using Machine Learning

Abstract

One of the major challenges encountered by cloud computing environments is the ability to handle dynamic workloads at a minimal energy usage and quality of service. This paper discusses a smart resource optimization system, which uses machine learning to forecast resource needs and assign virtual machines to the most efficient tasks. Through a proactive strategy, over-provisioning and under-provisioning that are traps of manual or rule-based scaling are reduced by the system. The method is concentrated on the combination of predictive modelling and automated scaling triggers. The studies apply the CloudSim Plus simulation toolkit as an environment modelling tool and Google Cluster Data traces as the main dataset. In particular, the investigation runs a portion of these traces to recreate heterogeneous real-world task requirements. The outcomes show that energy spending and rejection rates of tasks are significantly lowered as compared to traditional methods of allocation which are not dynamic. The metrics used in the performance are CPU utilization and memory overhead. As in the paper, machine learning models can dramatically improve the operational performance of the large-scale cloud infrastructures by making real-time data-driven decisions, which forms a solid basis behind the next-generation autonomous cloud management systems.