{"title":"自我意识绩效模型的愿景","authors":"Johannes Grohmann, Simon Eismann, Samuel Kounev","doi":"10.1109/ICSA-C.2018.00024","DOIUrl":null,"url":null,"abstract":"Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.","PeriodicalId":261962,"journal":{"name":"2018 IEEE International Conference on Software Architecture Companion (ICSA-C)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"The Vision of Self-Aware Performance Models\",\"authors\":\"Johannes Grohmann, Simon Eismann, Samuel Kounev\",\"doi\":\"10.1109/ICSA-C.2018.00024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.\",\"PeriodicalId\":261962,\"journal\":{\"name\":\"2018 IEEE International Conference on Software Architecture Companion (ICSA-C)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Software Architecture Companion (ICSA-C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSA-C.2018.00024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Software Architecture Companion (ICSA-C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSA-C.2018.00024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.