The Process and Model Innovation of Ideological Education Network Communication in Colleges and Universities Based on Cloud Computing

Abstract

The purpose is to improve the power and innovate the communication mode of mainstream I&P (Ideological and Political) education in C&U (Colleges and Universities). The opportunities and challenges that I&P education is facing or will face in media times are analyzed from three factors: the subjective, the mediator, and the environment, which affect the power of mainstream I&P in C&U. Educational means, carriers, resources, places and times, and the interactions between educators and the educated can bring opportunities for the improvement of the educational power of C&U. However, there are great challenges in all aspects of the mainstream ideology, such as education methods, education ideas, education content, education leadership and discourse power, and network public opinion control. Finally, a series of measures are proposed to improve the power of mainstream I&P education in C&U in media times, and they are updating the concept of media education, strengthening the ideological guidance, ensuring the direction of mainstream I&P education, and optimizing the media environment so that a more perfect innovative mode of I&P education is constructed. The research enriches and develops the theory of mainstream I&P education in C&U, innovates the methods of mainstream I&P education in C&U, and enhances the power of mainstream I&P education.

Design and Application of Cloud Resource-Based Ideological and Political Online Course Resource Platform

“…To do so, the cloud-computing simulation platform, CloudSim is utilized. Zhan [16] describes a technique for generating a virtual machine set where the size of each task in the task set is [2000, 150,000] and the execution speed of the virtual machine is in the range [200,1500] when using the approach. e execution time of the task on separate virtual machines is calculated according to the size and the speed of the task with which it is executed on each virtual machine.…”

“…Intelligent algorithms, according to current research directions, are the most promising approach to resolving the cloud-computing resource scheduling challenge at the moment. Such algorithms include particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithms (GA), and simulated annealing algorithms (SA), among others [14][15][16][17][18][19][20]. Its benefits include a smaller number of adjustable parameters and a faster convergence rate.…”