OpenStack 云平台的实现 CloudSim仿真试验 私有云平台的搭建和资源虚拟化实现 SOA接口层设计和实现 基于FUTG的资源调度算法研究

In an unknown environment, autonomous mobile robots rely on sensors to continually obtain information about the surrounding environment, discern the location of obstacles, make calculations and make decisions independently. The existing navigation algorithms are prone to repetition on the rigid path in the face of complex environment such as U-shape, which leads to the navigation can not continue. To this end, this paper presents a local optimization navigation algorithm based on fuzzy logic, using "recognition - memory" strategy to process the sensor information. In the path planning to retain the location of the recent path and angle characteristics and other related resources, the formation of "memory." When the current planning path forms a dead zone and runs repeatedly, "identification" is formed and the path and navigation decision are re-planned to avoid obstacles colliding. The simulation experiments under Webots Pro and Matlab show that the mobile robot can effectively avoid and avoid the dead zone under the guidance of fuzzy rules and realize better autonomous navigation..

在云环境下的大数据中心中,虚拟机数目和虚拟机的负载会随着用户和应用的需求而时常发生变化。虚拟机需要进行动态资源调整,及时移除系统中的热点资源,从而达到整个系统的负载均衡。通过对云资源分配的理论研究,获取到First-Fit贪心算法和Round Robin轮询算法等。将它们应用到一些云系统中虽然能够在短时间内解决问题,但存在资源利用率和负载均衡等方面的问题。文中提出一种基于博弈论的FUTG(Fairness-Utilization Tradeoff Gme)云资源调度算法。该算法打破了固定数量的资源分配瓶颈,将QoS因素纳入考量范围,解决了资源利用率以及资源分配的公平性这两个优化目标的资源调度问题。仿真实验结果表明,FUTG算法能够显著提高动态资源调度的有效性和动态负载下资源使用的执行效率。