Highly Distributable Associative Memory Based Computational Framework for Parallel Data Processing in Cloud

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.