Data centers are located centralized to do computation and accessing huge amount of data by the network devices which are interconnected to form the network path. Servers are stacked, data storage is placed in them. Data server backup and server redundancies are the recovery mechanisms implemented. Data centers compute, store, distribute the data by processing them and the data center controls all the interconnected network equipment in the distributed network. In current, RAID system is implemented to avoid the service disruptions due to disk failures, the availability of system and services are achieved with this expensive model. But still the availability is lost, and service disruptions happen due to disk failures, the machine learnings models to be used to predict the disk failures well in advance. Data center has increased usage of system with increased data storage, the failure in disc makes the system failed and down time increases. Analysis on the methods of problems in disk and methods of disk availability and predict the disk failure is the main goal. Various machine learning models are identified and discussed along with the SMART parameters for measuring the failure of the disk. Improved method of Ensembling of trees, random forest and boosting techniques are also discussed.

数据中心集中放置，通过互连形成网络路径的网络设备进行计算和访问海量数据。服务器堆叠在一起，数据存储放置在其中。数据服务器备份和服务器冗余是实施的恢复机制。数据中心通过处理数据来计算、存储、分发数据，数据中心控制分布式网络中所有互连的网络设备。目前，RAID 系统的实施是为了避免由于磁盘故障导致的服务中断，通过这种昂贵的模型来实现系统和服务的可用性。但是仍然会丢失可用性，并且由于磁盘故障而导致服务中断，机器学习模型用于提前预测磁盘故障。随着数据存储量的增加，数据中心增加了系统的使用率，磁盘故障使系统出现故障，停机时间增加。分析磁盘问题的方法和磁盘可用性的方法并预测磁盘故障是主要目标。确定并讨论了各种机器学习模型以及用于测量磁盘故障的 SMART 参数。还讨论了改进的树集成方法、随机森林和增强技术。