Abstract
One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. In this regard, associative memory concepts open a new pathway for accessing data in a highly distributed environment that will facilitate a parallel-distributed computational model to automatically adapt to the dynamic data environment for optimized performance. With this in mind, this paper targets a new type of data processing approach that will efficiently partition and distribute data for clouds, providing a parallel data access scheme that enables data storage and retrieval by association where data records are treated as patterns; hence, finding overarching relationships among distributed data sets becomes easier for a variety of pattern recognition and data-mining applications. The ability to partition data optimally and automatically will allow elastic scaling of system resources and remove one of the main obstacles in provisioning data centric software-as-a-service in clouds.
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Basirat, A.H., Khan, A.I., Srinivasan, B. (2014). Highly Distributable Associative Memory Based Computational Framework for Parallel Data Processing in Cloud. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_6
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DOI: https://doi.org/10.1007/978-3-319-11569-6_6
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