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Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Computer and Information Science.
2013 (Bokmål, Norwegian; Norwegian Bokmål)MasteroppgaveStudent thesisAlternative title
Searching in the clouds (English)
Abstract [nb]

This thesis has focused on how to process and store big data in the cloud, with a special focus on challenges on creating an information retrieval system and how distributed information retrieval methods can be used in the cloud. After evaluating three cloud platforms, Windows Azure was chosen because it gave more hardware resources in the free trial than the others, and due to the fact that it had an emulator that could be used to set up the system locally before testing it on the cloud. The search engine should also be chosen, but since Windows Azure was the preferred platform, the search engine choices was limited to those that were created in the .NET languages. I ended up with Lucene.NET because it is a powerful search tool. In addition, Lucene.NET is open source. The evaluation was done on a distributed information retrieval sys- tem that had a server-client set up, and used partial indexes that was distributed out to the clients. The evaluation was done with a small data set to nd optimization problems that has to be attended when creating a distributed system that handles large amounts of data. I carried out four evaluations on four dierent clients. The results revealed optimization problems that was special for the cloud, and that has to be attended when creating a distributed system that has to process and store big data in the cloud. Also, since scaling systems in the cloud is easier, the recommendation was that scaling of the clients should be dependent on how much Azure Cache is left on the clients due to a optimization problem that has to do with the search speed of the search engine. With some more tweaking and solving these optimization problems, the Cloud should provide an advantageous place to process and store big data.

Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2013. , 71 p.
URN: urn:nbn:no:ntnu:diva-23043Local ID: ntnudaim:6801OAI: diva2:656459
Available from: 2013-10-15 Created: 2013-10-15 Last updated: 2013-10-15Bibliographically approved

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