基于改进遗传算法的云教学平台研究

Research on the improved genetic algorithm applied to cloud computing aided teaching platform

云计算技术迅猛发展,云计算辅助教学平台应运而生,具有网络化的海量教学数据资源存储与计算功能和瘦客户端等显著优点,云辅助教学平台数据量和用户量巨大的特点决定了其作业类型的多样性和数据密集性,云辅助教学平台的设计重点在高效率的资源管理和调度。文中设计云计算辅助教学平台的体系结构,并对云平台作业调度的原有自适应遗传算法做出改进,以传统遗传算法做基础,综合数据公平和本地性选择遗传基因,相比较传统算法,在响应用户需求上更高效。仿真实验结果显示改进后算法更能体现公平性、并提高了效率,更适于云计算机环境。

With the rapid development of cloud computing technology, cloud computing aided teaching platform was generated, which has many significant advantages of massive teaching data storage and thin client. Since cloud computing platform has ＂multi-user and multi-job type, the paper proposes an improved genetic algorithm to improve the performance of cloud computing platform. The algorithm under the promise of guarantee consumer fairness, scheduled tasks to the node with data block of this tasks in order to reduce data translation cost, which arms to shorten all the task completion time and tries hard to improve the consumer satisfaction. Through the simulation analysis of the two algorithms, it is shown that improved genetic algorithm outperforms previous genetic algorithms in term of the job response time and fairness and consumer satisfaction, and is better adapted to the cloud computing environment.