基于博弈论的云资源调度算法

Cloud Resource Scheduling Algorithm Based on Game Theory

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。

In a large data center in a cloud environment,the number of virtual machines and the load of virtual machines change frequently with the needs of users and applications.The virtual machines need to make dynamic resource adjustments to remove hotspot resources in the system in time and implement load banlancing for the entire system.Now through theoretical research on cloud resource allocation,we have obtained such applications as First-Fit greedy algorithm and Round Robin polling algorithm that can be applied to some cloud systems to solve problems in a short time,but they have the problems of resource utilization and load.Therefore,this paper proposed a fuzzy-future-memory tradeoff(GMO)cloud resource scheduling algorithm based on game theory.The algorithm breaks a fixed number of resource allocation bottlenecks,takes QoS into consideration,and solves problems of resource utilization and resource allocation fairness.Simulation results show that FUTG algorithm can significantly improve the effectiveness of dynamic resource scheduling and the efficiency of resource usage under dynamic load.