Miss Muna Al-Saadi
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Optimising information storage for network performance analysis
Historical Information about traffic and network performance is critical for long-term monitoring of a computer network. A significant challenge in this context is balancing the amount of information collected and the limited storage capacity while maintain the relevant information about the performance as well as any relevant raw and contextual data for further analysis. Prior research was successful in summarising information about each connection or flow using netflow, analysing and storing the performance for individual connections, or traffic sampling; in parallel, storage efforts were channelled towards various database structured solutions. This project aims to advance the current state of the art by capturing raw traffic information that is subsequently filtered, pruned, or clustered in order to reduce the impact on storage. The underlying mechanisms will rely on Hadoop for storage of information, with follow-up analysis being able to reduce this information to more manageable volumes with minimum or no loss of data.Miss Muna Al-Saadi
The project will start with a review of the state of the art in the areas of network performance data collection and analysis as well as Hadoop storage. After establishing a baseline, the project will propose a set of methods for clustering and reducing the data in order to maintain the level or resolution required by performance analysis; in the next stage, the methods will be lined to the Hadoop infrastructure in order to optimise the performance and efficiency of the data reduction process. The project will then propose a framework to integrate the entire process, from raw data collection to long term storage.
Director of studies: Dr Bogdan Ghita
Other supervisors: Dr David Lancaster
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