Design And Implementation Of Disk Failure Prediction System Based On Machine Learning

With the rapid development of the Internet and the rapid growth of data volume,emerging technologies such as cloud computing,the Internet of Things,and big data and cloud storage are in the ascendant,and the smooth development of these technologies requires the support of massive storage systems.In today's era of knowledge explosion,data as a source of information for knowledge determines the survival of an enterprise to a certain extent.Therefore,the safe storage of large amounts of data is also the key to ensuring the stable operation of various businesses.Disks are still the mainstream equipment in the storage field today,and the running status of disks directly affects the reliability and availability of storage systems.Once the disk fails,it may cause business failures and lead to unavailability of data services.In severe cases,it may also lead to data loss,damage to the reputation of the enterprise,and irreparable losses to the enterprise.The disk failure prediction system based on machine learning aims to utilize the unified standard SMART(Self-Monitoring Analysis and Reporting Technology,or “self-monitoring,analysis,and reporting technology”)feature data manufactured by most manufacturers to realize the prediction of disk failures.Complete active fault tolerance of disk failure,reduce or even avoid data loss caused by disk failure.The system has completed many functions such as user management function,disk failure prediction function,log management function,statistical information function,disk alarm,etc.,and realized the landing of the disk failure prediction system combined with the company's data characteristics.In the construction of the system,the establishment of the disk operation database was also considered.Among them,the core function of the system is the disk failure prediction function,including the start prediction module,the pause prediction module,and the prediction model training module;the other three parts are all auxiliary to this part,and the user management module is responsible for the management of users using the system,Including many basic functions such as user login,information viewing,password modification,logout,etc.The log management module records related logs of user account processing on the one hand,and records disk failure prediction history and processing operations on the failed disk on the other hand The statistical information module records the operating status of the various services of the system,the operating status of the disk,and the statistical information of the best model for the disk failure prediction model.In my project,I mainly study the related functions of the disk prediction module.The front-end triggers the disk failure prediction function to start related services of the business logic layer.The related services of the business logic layer include multiple functions such as disk operation data collection service,data preprocessing service,disk state prediction service and disk state prediction,etc.,and provide interface packaging for the upper layer to realize the user's understanding of the prediction effect and capture The state of the disk takes measures to protect the data.All the services provided by the business logic layer are based on the database of the system disk operating characteristics,and the construction of the underlying database is also part of the work,which involves the use of Flume,Kafka and other technologies for disk operation logs distributed in different areas Be processed.The functional part of the business logic layer of the system is mainly implemented in Python.Through the offline exploration and processing of data features,effective machine learning algorithms in related fields are selected,and finally the prediction of disk failure is realized,and the most can be selected by comparison.Reliable results,achieving high accuracy with low false alarm rates.And encapsulate this series of modules into microservices to provide services to the upper layer,on the one hand,ensure that the front-end business can run normally during the upgrade iteration,on the other hand,ensure that the services are independent of each other,the logic is clear,and the state of loose coupling is maintained.