(untitled) (International Journal of Engineering and Computer Science, 2017)
–Any large unstructured data sets with sizes beyond the ability of the software tools to manage and process within a tolerable elapsed time is rightly observed as bigdata. Cloud computing is delivery of on demand computing resources from application to data center over internet. Combining these two strong reliable platforms helps in tackling extraveneous real time problems and obtaining solutions for it. Cloud embedded bigdata supports inexpensive reliable storage and tools for analyzing structured and unstructured, semi streaming, click streaming and various types of data. The existing system tends to be more costlier because of cloud deployment costs and it is not elastic in nature. The subjective nature of cloud delivery to incoming data streams pulls back the efficiency of the system. The paper aims to minimize the cost for cloud adoption by determining the cloud adoption factors from Net present value computation and derives a mathematical expression for ‘α’(Cloud adoption factor). It also addresses the issues that affect the performance issues of bigdata by implementing subordinate virtual cloud mechanism to overcome the addressed bottlenecks.