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In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago.

This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project.

The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide.

In the real world, building the high quality cloud computing framework is the challenge for the researcher in the present scenario where on demand service is required. The services which are performing the non-functional activities are referred to as Quality-of Service (QoS). The experience of real-world usage services are generally required to obtain the QoS. Many organizations offer various cloud services, such as Amazon, HP and IBM, to the customers. No technique is available to measure the real-world usage and estimate the ranking of the cloud services. From the customer side, it is a very tough job to choose the right cloud service provider (SP), which fulfills all the requirements of the customers. To avoid the confusion to select the right CSP, this paper proposes QoS ranking prediction methods such as Cloud Rank1, Cloud Rank2 and Cloud Rank3. Various experiments are done on the real-world QoS data by using EC2 services of Amazon and providing healthy results and solutions.