Abstract: Current serverless computing providers use a coupled resource allocation strategy with low flexibility and a fixed CPU-to-memory allocation ratio. As more types of functions are deployed to the serverless computing platform, the coupled strategy can not satisfy the wide range of resource requirements for these functions. Due to the small granularity of resource allocation and high deployment density in serverless functions, if CPU and memory resource allocation are decoupled, the problem of resource configuration space explosion needs to be solved. In this paper, we present Semi-Share, a decoupled resource manager for serverless functions, which can find the optimal resource configurations for functions while reducing the interference between co-located functions. To solve the resource configuration space explosion problem, Semi-Share builds a two-layer resource allocation architecture, which divides the resource configuration space into multiple subspaces to reduce problem complexity. The first layer is the function cluster, which is based on the resource preference and historical load information of the functions. The resource configuration space is divided according to these clusters. The second layer is resource allocation, which leverages the Bayesian optimization and weighted scoring function to guide Semi-Share to search in the right direction in the configuration space and reduce the time overhead. The experiment results show that Semi-Share greatly reduces the search time of searching the optimal resource configuration by using the two-layer architecture, reduces the average configurations sample by 85.77% compared with the widely used gradient descent search method, and improves the function performance by 42.72% on average. Compared with COSE, a coupled resource allocation system that also uses Bayesian optimization, Semi-Share can improve the function performance by 32.25% on average.