{"title":"基于基准的云端自动扩展Web应用的成本分析","authors":"Luciano Ocone, M. Rak, Umberto Villano","doi":"10.1109/WETICE.2019.00027","DOIUrl":null,"url":null,"abstract":"Applications executed in the cloud can exploit its elasticity features, varying dynamically the amount of leased resources so as to adapt to load variations and to guarantee good quality of service. As auto scaling has severe implications on execution costs, making optimal scaling choices is of paramount importance. This paper presents an analysis method based on off-line benchmarking that allows to define scaling policies to be used by auto-scalers. The indexes obtained by benchmarking multiple deployment configurations can be used on-line, to scale the application making a trade-off between cost and user-perceived performance.","PeriodicalId":116875,"journal":{"name":"2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Benchmark-Based Cost Analysis of Auto Scaling Web Applications in the Cloud\",\"authors\":\"Luciano Ocone, M. Rak, Umberto Villano\",\"doi\":\"10.1109/WETICE.2019.00027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applications executed in the cloud can exploit its elasticity features, varying dynamically the amount of leased resources so as to adapt to load variations and to guarantee good quality of service. As auto scaling has severe implications on execution costs, making optimal scaling choices is of paramount importance. This paper presents an analysis method based on off-line benchmarking that allows to define scaling policies to be used by auto-scalers. The indexes obtained by benchmarking multiple deployment configurations can be used on-line, to scale the application making a trade-off between cost and user-perceived performance.\",\"PeriodicalId\":116875,\"journal\":{\"name\":\"2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WETICE.2019.00027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WETICE.2019.00027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Benchmark-Based Cost Analysis of Auto Scaling Web Applications in the Cloud
Applications executed in the cloud can exploit its elasticity features, varying dynamically the amount of leased resources so as to adapt to load variations and to guarantee good quality of service. As auto scaling has severe implications on execution costs, making optimal scaling choices is of paramount importance. This paper presents an analysis method based on off-line benchmarking that allows to define scaling policies to be used by auto-scalers. The indexes obtained by benchmarking multiple deployment configurations can be used on-line, to scale the application making a trade-off between cost and user-perceived performance.