针对云计算数据中心中，低效的资源管理产生的高能耗问题，提出一种基于模糊的(Q- learning(λ))强化学习算法，通过处理虚拟机放置(VMP)问题来解决云计算数据中心的高能耗开销问题。将当前状态下的虚拟机数量以及物理机利用率作为输入状态传入模糊控制器，并与强化学习(RL)算法相结合来执行对应相关的策略。该算法能够动态地将相关虚拟机分配到所对应的物理服务器上并且能够减少虚拟机迁移次数，优化资源利用率，在满足用户服务级别协议(SLA)的同时降低能源消耗。该算法能够应对工作负载波动的情况，并在满足SLA的期望服务质量(QoS)需求的同时，提供合适的VM部署(初始或重新映射)。实验结果显示，与Q-learning、Q-learning(λ)、Greedy和PSO放置算法相比，基于模糊的Q-learning(λ)算法的能源消耗显著减少且具有更快的收敛速度和一定的实用价值。


Addressing the issue of high energy consumption resulting from inefficient resource management in cloud computing data centers, a fuzzy-based Q-learning(λ) reinforcement learning algorithm is proposed to tackle the high energy expenditure by addressing the virtual machine placement (VMP) problem. This algorithm takes the number of virtual machines in the current state and the utilization rate of physical hosts as input states, which are then fed into a fuzzy controller and combined with a reinforcement learning (RL) algorithm to execute corresponding strategies. This algorithm dynamically allocates relevant virtual machines to their corresponding physical servers, reducing the number of virtual machine migrations, optimizing resource utilization, and lowering energy consumption while satisfying user service level agreements (SLAs). This algorithm  can handle fluctuating workload situations and provide appropriate VM deployment (initial or remap) while meeting the expected quality of service (QoS) requirements of SLAs. Experimental results show that compared to Q-learning, Q-learning(λ), Greedy and PSO placement algorithms, the fuzzy-based Q-learning(λ) algorithm significantly reduces energy consumption and has a faster convergence rate, demonstrating its practical value.


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