Microservice deployment in a cluster environment has become an essential way. As different kinds of services have different demands on resources such as CPU, memory, and disk, it makes the nodes in the cluster produce resource fragments and become biased in resource consumption. How to enhance cluster resource utilization and reduce cluster energy consumption has become a major challenge after the service level agreement (SLA) is guaranteed. This study takes the detailed tracking of nearly 20000 microservices released by Alibaba Group in 2021 as data samples and analyzes their cluster resource consumption characteristics from multiple dimensions, such as container resource utilization, node deployment characteristics, and resource consumption preferences. As a result, the study finds that biased resource consumption occurs in the cluster. Further analysis of container deployment in the nodes reveals that this phenomenon is exacerbated by the inappropriate allocation of container resources. In view of this, this study proposes a model based on a deep dual-Q network to optimize container resource allocation according to the real-time changes of upstream service resource demands. The experimental results are compared, and it is shown that the method can effectively increase container resource utilization and ameliorate the bias of node resource consumption while serving SLA.