基于Q-Learning的虚拟机动态伸缩算法

针对大规模云环境中业务量变化时平台服务质量和资源消耗的问题,提出一种基于Q-Learning的虚拟机扩容/缩容决策算法。将该问题转换为马尔科夫决策模型,为了在业务平台服务质量和资源消耗之间取得较好的平衡,智能体根据平台当前状态计算出最佳策略,执行决策并转到下一状态。仿真结果表明,该算法可根据业务量的变化实时作出伸缩决策,并提供最合适的虚拟机资源以满足业务需求,且能提高平台的稳定性。

Q-Learning Based Auto-scaling Algorithm for Virtual Machines

To solve the problem of service quality and resources cost when the service volume changed in large cloud environment,this paper proposes a scaling method for virtual machines based on the Q-Learning algorithm.First,the problem is modeled as a Markov decision process.In order to achieve a better balance between platform service quality and resources consuming,the agent outputs the best decision according to the current state of the platform.The platform executes the decision and transfers to the corresponding state.The simulation results show that the method can make scaling decisions in real time according to the change of service volume so as to provide appropriate resources of virtual machines matching the requirements of service.The method also can enhance the stability of the platform.