The Research On Indicator System Of Decision-Making For Private Cloud Migration Using Toe Framework

In recent years cloud computing in domestic has developed quickly, so more and more large enterprises begin to deploy their private cloud. Therefore they have faced a problem that whether the existing application system is suitable for migration to the cloud. In order to provide a scientific and rigorous solution to the problem, this paper has carried on the research as follows:1. Did exploratory study on the factors that would impact the cloud migration decision of existing systems.Before trying to solve the problem, we tried to dig out factors that may affect the cloud migration decision. Firstly, in Chapter2, we summarized research achievements of the scholars and research institutions. Combing with TOE framework, which was highly used by the researchers of studying IS technology adoption, we sorted out a table containing the influencing factors that was mentioned by many scholars. Secondly, in order to test and verify the factors, we used the method of expert interview. The experts came from various industries, such as cloud computing researchers, the employees of IT department in a big company that had deployed the private cloud, the engineers of cloud service providers. Finally, we modified the influencing factors table and got the final table, thus laid the theoretical foundation of the establishment of indicator system of decision-making for cloud migration.2. Built an evaluation indicator system of decision-making for the existing applications migrating to the cloud.After that, following the establishing principles of hierarchy indicator system, we built the evaluation indicator system. It contained three hierarchies. First was criterion layer, including Technology fitment, Business value, Risk exposure. Second was primary index layer, separately listed the indicators under each criterion. Technology fitment included six indicators, which were Integration, Efficiency, Customization, System Characteristic, Performance and Compatibility. Business value included Mission Critical, Cost Saving, Top Manager Support, Social Effect, Data Sensitive and Migration Cost. Risk exposure included Maturity Lack, Data Security, Control and Compliance. Third layer listed secondary indicators, which were used for the complement and explanation of primary indexes. There were totally31secondary indicators. As the importance of the indicators was not all in the same, so we needed to determine the weight of them.3. Though the empirical research, we verified the rationality of the indicator system.We chose the Base of one of Chinese operators as a case. It had deployed its private cloud Integrated Support Cloud. And we also chose the training management system as an under-evaluating case. We collected data though questionnaires which were sent to the member of the study and the related employers in South Base. There were25data samples in all. With the data, we used fuzzy comprehensive evaluating method, and finally we got a score group containing the scores of technology fitment, business value, and risk exposure. Different score groups represented different decision schemes. Comparing with the table of decision schemes, the conclusion that the training management system was suitable for migrating to the cloud was came up. As the system had been already migrated to the cloud, the rationality of indicator system of decision-making for private cloud migration had been tested successfully.