Jichao Wang, Ran Zhang, Guanqiang Qi, Lanqing Hong
{"title":"非分布环境下硬盘故障预测的启发式irm方法","authors":"Jichao Wang, Ran Zhang, Guanqiang Qi, Lanqing Hong","doi":"10.1109/IEEM50564.2021.9672905","DOIUrl":null,"url":null,"abstract":"The hard disk drives (HDD) are essential devices lying in primary layers of diverse information infrastructure. Long-term disk failure predictions are crucial to the stability and robustness of storage systems for data centers. In this paper, a domain adaption method is developed to improve prediction performance in out-of-distribution disk datasets. We propose heuristic invariant risk minimization (HIRM) with a new loss function to deal with imbalanced data. The HIRM combined with machine learning models are verified to promote the accuracy and stability in out-of-distribution (OoD) data. When hard disks with new SMART feature distribution are introduced into the data center, the proposed HIRM algorithm achieves better results than vanilla neural networks. A numerical example using the data from the BackBlaze data center is shown to illustrate the application of our HIRM model. The aims of each person are different.","PeriodicalId":6818,"journal":{"name":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":"42 6","pages":"1661-1664"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Heuristic-IRM Method on Hard Disk Failure Prediction in Out-of-distribution Environments\",\"authors\":\"Jichao Wang, Ran Zhang, Guanqiang Qi, Lanqing Hong\",\"doi\":\"10.1109/IEEM50564.2021.9672905\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The hard disk drives (HDD) are essential devices lying in primary layers of diverse information infrastructure. Long-term disk failure predictions are crucial to the stability and robustness of storage systems for data centers. In this paper, a domain adaption method is developed to improve prediction performance in out-of-distribution disk datasets. We propose heuristic invariant risk minimization (HIRM) with a new loss function to deal with imbalanced data. The HIRM combined with machine learning models are verified to promote the accuracy and stability in out-of-distribution (OoD) data. When hard disks with new SMART feature distribution are introduced into the data center, the proposed HIRM algorithm achieves better results than vanilla neural networks. A numerical example using the data from the BackBlaze data center is shown to illustrate the application of our HIRM model. The aims of each person are different.\",\"PeriodicalId\":6818,\"journal\":{\"name\":\"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)\",\"volume\":\"42 6\",\"pages\":\"1661-1664\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEM50564.2021.9672905\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM50564.2021.9672905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Heuristic-IRM Method on Hard Disk Failure Prediction in Out-of-distribution Environments
The hard disk drives (HDD) are essential devices lying in primary layers of diverse information infrastructure. Long-term disk failure predictions are crucial to the stability and robustness of storage systems for data centers. In this paper, a domain adaption method is developed to improve prediction performance in out-of-distribution disk datasets. We propose heuristic invariant risk minimization (HIRM) with a new loss function to deal with imbalanced data. The HIRM combined with machine learning models are verified to promote the accuracy and stability in out-of-distribution (OoD) data. When hard disks with new SMART feature distribution are introduced into the data center, the proposed HIRM algorithm achieves better results than vanilla neural networks. A numerical example using the data from the BackBlaze data center is shown to illustrate the application of our HIRM model. The aims of each person are different.