Abstract: 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.

Key words: cloud computing, virtual machine placement, reinforcement learning, fuzzy system