Serverless computing is an emerging cloud computing model based on the “function as a service (FaaS)” paradigm. Functions serve as the fundamental unit for deployment and scheduling, providing users with massively parallel and automatically scalable function execution services without the need to manage underlying resources. For users, serverless computing helps them alleviate the burden of managing cluster-level infrastructure, enabling them to focus on business-layer development and innovation. For service providers, applications are decomposed into fine-grained functions, leading to significantly improved scheduling efficiency and resource utilization. The significant advantages have swiftly drawn the attention from the industry and propelled serverless computing into popularity. However, the distinct computing mode of serverless computing, divergent from traditional cloud computing, along with its stringent limitations on various aspects of tasks, poses numerous obstacles to application migration. The escalating complexity of migrated tasks also imposes higher performance requirements on serverless computing. Therefore, performance optimization technology for serverless computing systems has emerged as a critical research topic. This study reviews and summarizes research efforts on performance optimization of serverless computing from four perspectives, and introduces existing system. Firstly, this study introduces the optimization technologies for typical tasks, including task adaptation and system optimization for specific task types. Secondly, it reviews the optimization work on sandbox environments, encompassing sandbox solutions and cold start optimization methods, which play a crucial role in the execution of serverless functions. Thirdly, it provides an overview of the optimization in I/O and communication technologies, which are major performance bottlenecks of serverless applications. Lastly, it briefly outlines related resource scheduling technologies, including platform-oriented and user-oriented scheduling strategies, which determine system resource utilization and task execution efficiency. In conclusion, it summarizes the current issues and challenges of performance optimization technologies of serverless computing and anticipates potential future research directions.