A Novel Topology Optimization Theory and Parallel Data Analysis Model Based Resource Scheduling Algorithm for Cloud Computing

Background: In this paper, we theoretically analyze the topic of topology optimization theory and parallel data analysis model based on resource scheduling algorithm for cloud computing. Cloud computing is a virtual computing resource pool, which provides dynamic deployment and allocation of these resources to the user via the Internet. Methods: Cloud storage is to focus on providing internet-based online service. Users don't need to consider storage capacity, the types of storage devices and the data storage location as well as data availability. The challenge for the cloud system is a resource scheduling algorithm which will dramatically influence systematic performance. To deal with the mentioned challenges, our research proposes the following novel research. Results: (1) We analyze the basic topology optimization methodology to propose revised modification approach for cloud system which is fitter for the cloud environment. (2) To deal with the large-scale data, we propose the parallel data processing model to separate computation to the different sub-systems which will enhance the system robustness and capability. (3) We modify traditional resource scheduling algorithm with the prior theory which enhances the overall performance mainly to the orientation of matrix to carry on the design of the optimal solution for the various optimization standards. Conclusion: As is indicated in the experimental part, our methodology outperforms other state-of-the-art algorithm.