Under the Cloud: A Novel Content Addressable Data Framework for Cloud Parallelization to Create and Virtualize New Breeds of Cloud Applications

Abstract

Existing data management schemes in clouds are mainly based on Google File System (GFS) and MapReduce. Problems arise when data partitioning among numerous available nodes therein. This research paper explores new methods of partitioning and distributing data, that is, resource virtualization in cloud computing. Loosely-coupled associative computing techniques, which have so far not been considered for clouds, can provide the break through needed for their data management. Applications based on associative computing models can efficiently utilize the underlying hardware to scale up and down the system resources dynamically. In doing so, the main hurdle towards providing scalable partitioning and distribution of data in the clouds is removed, bringing forth a vastly superior solution for virtualizing data intensive applications and the system infrastructure to support pay on per-use basis.