Energy efficiency is one of the important parameters in cloud computing which is managed by the data centres. Data centres are computer warehouses that are responsible for storing large volumes of data to deal with the daily transaction handling needs of different productions. Effective scheduling for the execution of the request on machines is still a problem. In addition, the power consumption, as well as management of the node clusters is also a problematic situation when the CPU utilisation increases up to the limit. In this paper, efficient minimum execution and completion time scheduling are accomplished by using a machine learning approach for effectual CPU usage and service level agreement fulfilment in data centres, considered in terms of average accuracy which will reduce costs for the maintenance of the data centres in real-time scenarios. The simulation of the proposed work is achieved and the performance is evaluated in terms of power consumption and CPU usage. The proposed research utilises the neural network and linear regression analysis to perform the classification and compares the performance for the efficient CPU usage.

能源效率是由数据中心管理的云计算的重要参数之一。数据中心是计算机仓库，负责存储大量数据，以应对不同生产的日常交易处理需求。在机器上执行请求的有效调度仍然是一个问题。另外，当CPU利用率上升到极限时，功耗，以及节点集群的管理也是一个问题。在本文中，通过使用机器学习方法来实现高效的最小执行和完成时间调度，以实现数据中心中有效的 CPU 使用和服务水平协议的履行，从平均精度的角度考虑，这将降低实时场景中数据中心的维护成本。实现了拟议工作的模拟，并根据功耗和 CPU 使用率评估了性能。拟议的研究利用神经网络和线性回归分析来执行分类并比较有效 CPU 使用率的性能。