## 1 Introduction  
In recent years, GIS platforms based on cloud architecture have been deeply developed and play an important role in supporting off-site users to achieve efficient and reliable spatio-temporal data querying, browsing, analysis, and other tasks [1, 2, 3]. However, under conditions of limited-service resources, in the process of providing services to users, when there are a large number of task access requests on ports or interfaces in a short period of time, the performance bottleneck of GIS platforms in high-concurrency scenarios will occur. For this reason, how to improve the quality of service [4] and enhance the stability and smoothness of services in such environments has been widely discussed by scholars [5, 6]. The study of high-concurrency problems is commonly found in the field of communication network traffic transmission [7]; for this reason, scholars mostly introduce the method of high-concurrency processing of network traffic when solving similar problems existing in cloud platforms in the field of GIS [8]. In general, the related research can be divided into two main categories, one is the multi-server high-concurrency processing method [9] and the other is the single-server high-concurrency processing method [10]. In the former case, due to the large number of available service resources, the key to solving the problem lies in increasing the number of hardware, that is, multiple hardware resources are used to disperse users' requests, such as the common load balancing algorithm [11]. In the latter case, due to the limited available service resources, the key to solving the problem is to improve the task traffic-shaping algorithms [12], that is, limiting the utilization rate of the entire resource by the task to alleviate the network congestion in the case of network congestion. For example, traffic policing [13] and traffic shaping [14]. The study of this paper belongs to the latter category. Compared to multi-server high-concurrency processing methods [15; 16], there is limited research on single-server high-concurrency processing techniques. For single servers, existing studies still mainly primarily rely on classical traffic-shaping algorithms. [PERSON] [17] introduced a leaky bucket algorithm, which regulates unregulated task requests by limiting their output rate. The leaky bucket algorithm is one of the simplest and most effective methods in the task traffic-shaping technique. However, it is not well-suited for handling bursty traffic, as tasks that do not exceed the rate limit are still processed at a fixed rate, reducing the responsiveness of task responses. To address this issue, [PERSON] [18] proposed the token bucket algorithm, which operates on the principle that tokens are added to a bucket at a constant rate. If a request needs processing, it must first obtain a token from the bucket. By controlling the tokens, the task processing rate can also be controlled. The token bucket algorithm offers a robust solution for handling sudden or instantaneous tasks, making it a frequently used and highly effective method in task traffic-shaping research. In addition, [PERSON] et al. [19] proposed enhanced token bucket algorithms, i.e., single-rate three-color marking algorithm and double-rate three-color marking algorithm, and [PERSON] [20] proposed a hierarchical token bucket algorithm based on QoS awareness, providing valuable insights for managing tasks with varying priorities or self-similar characteristics. However, many researchers still continue to use the classical token bucket algorithm for task traffic-shaping in the case of tasks with no priority or similarity [21]. Although high-concurrency processing algorithms originating from communication network traffic transmission offer effective solutions for task traffic shaping in GIS platforms [22], tasks in GIS platforms are more complex and diverse, with varying demands on system resources compared to short-term network traffic transmission issues [23]. For a single server, computing resources are limited.