{"title":"An user-centric billing model for cloud computing","authors":"R. Anand Kumar, R. Mittal","doi":"10.1109/ICCCTAM.2012.6488086","DOIUrl":null,"url":null,"abstract":"This paper proposed a more user centered billing model that allows the user to make an informed decision on selecting from a set of cloud offerings or between offerings from different clouds. An architecture for Infrastructure as a Service for such a billing model is presented. This billing model requires the estimation of costs for various configurations of computational requirement. Such a scenario would require estimation of application execution time. This paper evaluates the Worst Case Execution Time techniques available for real time systems for the cloud computing context. A neural network based application execution time based on non-mission critical systems, user specified parameters (e.g., CPU, operating system, and database parameters), historical training data and limited simulations is presented here.","PeriodicalId":111485,"journal":{"name":"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCTAM.2012.6488086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposed a more user centered billing model that allows the user to make an informed decision on selecting from a set of cloud offerings or between offerings from different clouds. An architecture for Infrastructure as a Service for such a billing model is presented. This billing model requires the estimation of costs for various configurations of computational requirement. Such a scenario would require estimation of application execution time. This paper evaluates the Worst Case Execution Time techniques available for real time systems for the cloud computing context. A neural network based application execution time based on non-mission critical systems, user specified parameters (e.g., CPU, operating system, and database parameters), historical training data and limited simulations is presented here.