Research on Data Asset Management Strategy for Cloud Computing Environment

The current cloud computing deployment environment is intricate and complex, and blockchain technology has become an important method to solve the data asset management problem due to its excellent properties such as decentralization, openness, autonomy, anonymity, and information non-tampering. In this paper, CloudDPoS, a DPoS consensus algorithm for cloud data traceability blockchain, is constructed based on the practical Byzantine fault-tolerant algorithm to solve the consensus reaching problem in the presence of nodes failures. In order to reduce the resource consumption of traditional consensus algorithms and improve its adaptability in the cloud, and at the same time, a node replacement mechanism is introduced to guarantee the fault tolerance of the consensus process. Finally, performance testing simulation experiments are conducted and the platform data asset management is evaluated for perfection. In this paper, the number of blocks generated per second by the platform at different sending frequencies ranges from 46 to 53, which is a great improvement compared to the block generation rates of Bitcoin and Ether. In the time delay experiment, when the number of system requests per second is 50 times, the average time delay of the system is 1.67s, the minimum time delay is 0.69s, and the maximum time delay is 2.84s. With the increasing frequency of sending, the time delay is also increasing, but the average time delay is basically around 10s, and there is no significant increase at the later stage, which ensures a reasonable response efficiency to the user interaction and does not consume the user interaction behavior. The average time delay is basically about 10s, and there is no significant increase at the later stage, which ensures the reasonable response efficiency to user interaction behavior and does not consume too much memory or CPU resources. Evaluating the perfection of the data asset management strategy proposed in this paper, the final comprehensive score is 4.16, which reflects the strong guarantee of this paper’s platform on the dimensions of numerical asset application, data security, and data governance.