{"title":"利用粗糙集模型优化云服务提供商调度","authors":"Mehul Mahrishi, Aitha Nagaraju","doi":"10.1109/ICCCTAM.2012.6488103","DOIUrl":null,"url":null,"abstract":"The applications submitted to cloud middle ware have been distributed to the CSPs based on the available CSPs in the cloud environment to categorize the service CSP providers with this work we are trying to introduce a concept to find the optimal csp based on rough set based approach. IaaS provides a large amount of computational capacities to users in a flexible and efficient way. In the market various CSPs are available example Amazons elastic computing cloud offers virtual machine with 0.1 us dollars per hour similarly another cloud Google compute cloud offers virtual machine with 0.5 us dollars per hour then the cloud users need rating among the various Csps. In this research work we have been proposing an approach to provide the rating of CSPs based on the internal performance of Datacenters and virtual machines. In present situation day-by- day number of cloud service providers have been increasing drastically. In this scenario existing service providers scheduling need a mechanism to find the optimal service providers information to Service request scheduling using this information SRS can allocate the service to the respective optimal service providers. In this paper we studied the problem of dynamic request allocation and scheduling for context aware application deployed in geographically distributed data centers forming a cloud.","PeriodicalId":111485,"journal":{"name":"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Optimizing cloud service provider scheduling by using rough set model\",\"authors\":\"Mehul Mahrishi, Aitha Nagaraju\",\"doi\":\"10.1109/ICCCTAM.2012.6488103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The applications submitted to cloud middle ware have been distributed to the CSPs based on the available CSPs in the cloud environment to categorize the service CSP providers with this work we are trying to introduce a concept to find the optimal csp based on rough set based approach. IaaS provides a large amount of computational capacities to users in a flexible and efficient way. In the market various CSPs are available example Amazons elastic computing cloud offers virtual machine with 0.1 us dollars per hour similarly another cloud Google compute cloud offers virtual machine with 0.5 us dollars per hour then the cloud users need rating among the various Csps. In this research work we have been proposing an approach to provide the rating of CSPs based on the internal performance of Datacenters and virtual machines. In present situation day-by- day number of cloud service providers have been increasing drastically. In this scenario existing service providers scheduling need a mechanism to find the optimal service providers information to Service request scheduling using this information SRS can allocate the service to the respective optimal service providers. In this paper we studied the problem of dynamic request allocation and scheduling for context aware application deployed in geographically distributed data centers forming a cloud.\",\"PeriodicalId\":111485,\"journal\":{\"name\":\"2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"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.2012.6488103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","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.2012.6488103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing cloud service provider scheduling by using rough set model
The applications submitted to cloud middle ware have been distributed to the CSPs based on the available CSPs in the cloud environment to categorize the service CSP providers with this work we are trying to introduce a concept to find the optimal csp based on rough set based approach. IaaS provides a large amount of computational capacities to users in a flexible and efficient way. In the market various CSPs are available example Amazons elastic computing cloud offers virtual machine with 0.1 us dollars per hour similarly another cloud Google compute cloud offers virtual machine with 0.5 us dollars per hour then the cloud users need rating among the various Csps. In this research work we have been proposing an approach to provide the rating of CSPs based on the internal performance of Datacenters and virtual machines. In present situation day-by- day number of cloud service providers have been increasing drastically. In this scenario existing service providers scheduling need a mechanism to find the optimal service providers information to Service request scheduling using this information SRS can allocate the service to the respective optimal service providers. In this paper we studied the problem of dynamic request allocation and scheduling for context aware application deployed in geographically distributed data centers forming a cloud.