期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2013
卷号：4
期号：6
页码：988-994
出版社：TechScience Publications
摘要：Cloud computing plays a vital role in the next phase of IT enterprise. The management of data in the cloud computing environment is processed with the centralized data centers which might not be trustworthy. So, it is difficult to provide a security over a data storage in cloud computing. Several software specifications are introduced to get back the erroneous data from the dataset stored in the large data centers. One of the root causes of the software products frequently come up with poor, incomplete, or yet without any recognized specifications. In an attempt to get better program understanding, extended a iterative software pattern testing [1] in which output patterns are repeat regularly within a program trace, or diagonally multiple traces. Frequent iterative patterns provide recurrent set of program activities that are processed similar to the software specifications but do not challenge with the software engineers using the software nested pattern testing in cloud environment. To overcome the issues of storage security in cloud computing environment, a novel nested software pattern testing leads to not only more compact yet also a complete result with better efficiency. Nested Software Pattern (NSP) - block algorithm for nesting software sequential pattern testing is mainly determined over candidate’s generation and protection. NSP- BLOCK is an algorithm based on the concepts of memory blocks, works on exclusive occurrence of items in a sequential cloud database. A conception is a bigger software testing block can grasp the smaller of having same type in cloud zone to address the need of software engineers. Simulation experiments are conducted with various conditions in the cloud computing environment using Amazon EC2 dataset. Performance metric for evaluation of proposed NSP technique is measured in terms of Scalability, Pattern Prediction and run time (log scale).
