Abstract
Windows Azure provides an IaaS cloud service with virtual machines, web and worker roles and practically unlimited, pay-as-you-go storage options which can be used for applications requiring big data or parallel computing which is important in many fields including biology, astronomy, nuclear physics and economics.
When moving an application or computation task to the cloud it is very important to perform proof of concept performance testing and to carefully choose the proper building blocks for the given tasks. Windows Azure provides multiple data management options with a relational SQL database for transactional data access, Azure Tables for auto scalable storage of unstructured data, and a blob storage for storing large amounts of binary data which is easily mountable to a given virtual machine.
In this paper we present a general performance analysis of the Windows Azure cloud with focus on cloud storage options. We present an environment to perform automated testing of the major features of Azure storage and we also present the preliminary results and suggestions regarding the usage of the different services.
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Acknowledgments
The work reported in the paper has been developed in the framework of the project “Talent care and cultivation in the scientific workshops of BME” project. This project is supported by the grant TÁMOP-4.2.2.B-10/1–2010-0009.
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Hartung, I., Goldschmidt, B. (2014). Performance Analysis of Windows Azure Data Storage Options. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_57
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DOI: https://doi.org/10.1007/978-3-662-43880-0_57
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