Applying hybrid cloud systems to solve challenges posed by the big data problem

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Jeffrey N. Whitworth (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/
Advisor
Shanmugathasan Suthaharan

Abstract: The problem of Big Data poses challenges to traditional compute systems used for Machine Learning (ML) techniques that extract, analyze and visualize important information. New and creative solutions for processing data must be explored in order to overcome hurdles imposed by Big Data as the amount of data generation grows. These solutions include introducing hybrid cloud systems to aid in the storage and processing of data. However, this introduces additional problems relating to data security as data travels outside localized systems to rely on public storage and processing resources. Current research has relied primarily on data classification as a mechanism to address security concerns of data traversing external resources. This technique can be limited as it assumes data is accurately classified and that an appropriate amount of data is cleared for external use. Leveraging a flexible key store for data encryption can help overcome these possible limitations by treating all data the same and mitigating risk depending on the public provider. This is shown by introducing a Data Key Store (DKS) and public cloud storage offering into a Big Data analytics network topology. Finding show that introducing the Data Key Store into a Big Data analytics network topology successfully allows the topology to be extended to handle the large amounts of data associated with Big Data while preserving appropriate data security. Introducing a public cloud storage solution also provides additional benefits to the Big Data network topology by introducing intentional time delay into data processing, efficient use of system resources when data ebbs occur and extending traditional data storage resiliency techniques to Big Data storage.

Additional Information

Publication
Thesis
Language: English
Date: 2013
Keywords
Big Data, Cloud, Intrusion Detection, Storage
Subjects
Big data
Cloud computing $x Security measures

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