{"title":"基于自适应模型的工业数据中心性能管理","authors":"C. Franke, M. Fritzsche, Sergio Pacheco-Sanchez","doi":"10.1109/EASE.2010.13","DOIUrl":null,"url":null,"abstract":"System Management of large Data Centers addresses both the initial provisioning of software systems on top of the available infrastructure and the operational management of resources at runtime. The system management aims to optimize the provider's defined objectives, while satisfying the given system and customers constraints. In this paper, we propose an autonomous system management approach, which incorporates, if available, software models of business applications and the applications' management experiences from the past. We discuss existing approaches and present an extended management architecture that is built and trained at design-time and calibrated at runtime.","PeriodicalId":196497,"journal":{"name":"2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-Adaptive Model-Based Performance Management in Industrial Data Centers\",\"authors\":\"C. Franke, M. Fritzsche, Sergio Pacheco-Sanchez\",\"doi\":\"10.1109/EASE.2010.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"System Management of large Data Centers addresses both the initial provisioning of software systems on top of the available infrastructure and the operational management of resources at runtime. The system management aims to optimize the provider's defined objectives, while satisfying the given system and customers constraints. In this paper, we propose an autonomous system management approach, which incorporates, if available, software models of business applications and the applications' management experiences from the past. We discuss existing approaches and present an extended management architecture that is built and trained at design-time and calibrated at runtime.\",\"PeriodicalId\":196497,\"journal\":{\"name\":\"2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EASE.2010.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EASE.2010.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-Adaptive Model-Based Performance Management in Industrial Data Centers
System Management of large Data Centers addresses both the initial provisioning of software systems on top of the available infrastructure and the operational management of resources at runtime. The system management aims to optimize the provider's defined objectives, while satisfying the given system and customers constraints. In this paper, we propose an autonomous system management approach, which incorporates, if available, software models of business applications and the applications' management experiences from the past. We discuss existing approaches and present an extended management architecture that is built and trained at design-time and calibrated at runtime.