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How do I control cost of cloud infrastructures?

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Yes, we all know that cloud computing can be cheaper than paying for setting up and running your own server. But the dirty little secret is that figuring out your monthly bill isn’t easy. Because everything in the cloud is priced separately, it’s a bit like buying an American car in the 1980’s—when even the options had options. Some cloud providers don’t make pricing available until you sign up for their service. Others hide pricing schedules behind complex formulae. And therein lies the challenge for an IT manager who wants to try to find the best-priced cloud: you have to read the fine print, and make sure you understand what is billable, how it is measured and priced, and when the meter starts (and stops) running. Let’s look at where you can get more precise cost information, as well as examine a few of the growing number of third-party comparison services that can help you get more control over your cloud costs.

For many situations, the move to public cloud storage is a good use case to address abstraction and off-site storage. This could be for situations such as data protection, content delivery, and large file exchange. Simple cost savings with public cloud storage aren’t usually a leading discussion point however.

SO I have been using the azure 3 month trial, to test out whether I want to use Microsoft Azure to host a project I am working on, however I have been very confused as I have run out of "Geo Redundant Storage" in the first month and I don't really understand why. I have read this: https://www.windowsazure.com/en-us/pricing/details/ and the only thing I can make of it, is that it takes an average of how much storage you are using across a month, eg as long as I am using less then 35gb (for a 35gb limit) on average of storage space I am in the clear.

 

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  • "A cost-effective strategy for intermediate data storage in scientific cloud workflow systems", IPDPS (Conference). 2010. 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS 2010): Atlanta, Georgia, USA: 19-23 April, 2010: [proceedings]. Piscataway, NJ: IEEE.

Many scientific workflows are data intensive where a large volume of intermediate data is generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In this paper, we build an Intermediate data Dependency Graph (IDG) from the data provenances in scientific workflows. Based on the IDG, we develop a novel intermediate data storage strategy that can reduce the cost of the scientific cloud workflow system by automatically storing the most appropriate intermediate datasets in the cloud storage. We utilise Amazon's cost model and apply the strategy to an astrophysics pulsar searching scientific workflow for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly.

 

This paper consists of four major sections: The first section is a literature review of cloud computing and a cost model. The next section focuses on detailed overviews of cloud computing and its levels of services: SaaS, PaaS, and IaaS. Major cloud computing providers are introduced, including Amazon Web Services (AWS), Microsoft Azure, and Google App Engine. Finally, case studies of implementing web applications on IaaS and PaaS using AWS, Linode and Google AppEngine are demonstrated. Justifications of running on an IaaS provider (AWS) and running on a PaaS provider (Google AppEngine) are described. The last section discusses costs and technology analysis comparing cloud computing with local managed storage and servers. The total costs of ownership (TCO) of an AWS small instance are significantly lower, but the TCO of a typical 10TB space in Amazon S3 are significantly higher. Since Amazon offers lower storage pricing for huge amounts of data, the TCO might be lower. Readers should do their own analysis on the TCOs.

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