期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：8
DOI：10.14569/IJACSA.2018.090826
出版社：Science and Information Society (SAI)
摘要：One of the main challenges in cloud computing is to cope up with the selection of efficient resources in terms of cost. There are various cloud computing service providers which dynamically provide resources to the customers through different pricing policies. Based on the different APIs and pricing policies of the service providers, it becomes difficult for the customers to select the best service provider in terms of cost. In some cases, if the usage of the resources provided by a datacenter exceeds certain limit, then the providers cannot offer more resources to the customers as new VMs cannot be created. Hence, even if the customer chooses the best provider based on the least cost parameter, still there is no guarantee that the provider allocates complete resources to the customer. For this reason, I present system architecture that selects the best service provider based on the customer requirements mainly the cost. The proposed architecture also performs resource management by automatically providing new VMs from the available service providers in the inter cloud. The proposed system is based on five clouds i.e. Amazon EC2, Cloudsigma, Google, GoGrid, and Windows Azure. An interface is designed for obtaining the user requirements. These user requirements are matched with the design database of five cloud providers and based on the matched values; the catalog of optimal costs for each particular cloud is shown to the user. Then Cost Aware Resource Selection algorithm is run for determining the lowest optimal cost for Instance based approach and Quantity based approach. The algorithm tackles two domains of clouds for the algorithm i.e. Single Cloud and Multi Cloud.
关键词：Cloud computing; pay-as-you-go; infrastructure as a service; cost aware resource selection; virtual machines; hypervisor; instance based approach; quantity based approach; single cloud; multi cloud
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