International Journal of Advances in Computer Science and Its Applications
Author(s) : R.POORVADEVI , S.RAJALAKSHMI , S.RAMAMOORTHY
All the companies are nowadays migrating their applications towards cloud environment, because of the huge reduce in the overall investment and greatest flexibility provided by the cloud. The Cloud provides the larger volume of space for the storage and different set of services for all kind of applications to the cloud customers. There is not much delay and major changes required at the client level. The large amount of user data and application results stored on the cloud environment, will automatically make the data analysis and prediction process very difficult on the different clusters of cloud. It is always difficult to process, whenever a user required to analyze the data stored on the cloud as well as frequently used service by other cloud customers for the same set of query on the cloud environment.The existing data mining techniques are insufficient to analyze those huge data volumes and identify the frequent services accessed by the cloud users. In this proposed scheme we are trying to provide an optimized data and service analysis model based on Map-Reduce algorithm along with BigData analytics techniques. Cloud services provider can Maintain the log for the frequent services from the past. The service history analysis on multiple clusters to predict the frequent service. Through this analysis cloud service provider can recommend the frequent services used by the other cloud customers for the same query. This scheme automatically increases the number of customers on the particular cloud environment and effectively analyze the data which is stored on the cloud storage.