{"title":"神经网络在服务故障根本原因预测中的应用","authors":"R. Harper, P. Tee","doi":"10.23919/INM.2017.7987422","DOIUrl":null,"url":null,"abstract":"The principal objective when monitoring compute and communications infrastructure is to minimize the Mean Time To Resolution of service-impacting incidents. Key to achieving that goal is determining which of the many alerts that are presented to an operator are likely to be the root cause of an incident. In turn this is critical in identifying which alerts should be investigated with the highest priority.","PeriodicalId":119633,"journal":{"name":"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The application of Neural Networks to predicting the root cause of service failures\",\"authors\":\"R. Harper, P. Tee\",\"doi\":\"10.23919/INM.2017.7987422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The principal objective when monitoring compute and communications infrastructure is to minimize the Mean Time To Resolution of service-impacting incidents. Key to achieving that goal is determining which of the many alerts that are presented to an operator are likely to be the root cause of an incident. In turn this is critical in identifying which alerts should be investigated with the highest priority.\",\"PeriodicalId\":119633,\"journal\":{\"name\":\"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/INM.2017.7987422\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/INM.2017.7987422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The application of Neural Networks to predicting the root cause of service failures
The principal objective when monitoring compute and communications infrastructure is to minimize the Mean Time To Resolution of service-impacting incidents. Key to achieving that goal is determining which of the many alerts that are presented to an operator are likely to be the root cause of an incident. In turn this is critical in identifying which alerts should be investigated with the highest priority.
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