## 5 Discussion  
### Advantages of Adaptive Task Traffic-Shaping Algorithm  
GIS services often handle large volumes of spatial data, including vector, raster, and topographic datasets, which are inherently complex and computationally intensive. Tasks such as buffer analysis, overlay analysis, and path-finding involve sophisticated spatial computations and visualizations, while proximity analysis and spatial interpolation require efficient spatial indexing and query mechanisms. Additionally, many GIS applications (such as navigation and real-time monitoring) demand stringent response times, necessitating rapid data processing and result delivery. However, single-server architectures for high-concurrency GIS tasks exhibit critical limitations:  
1. Hardware Resource Constraints: Single-server computational capabilities are restricted by the hardware specifications of a single machine (e.g., CPU, memory, storage), leading to performance bottlenecks under high concurrency. 2. Task Queueing and Resource Contention: Under high concurrency, single servers process requests sequentially, causing task queueing. Simultaneously, multiple concurrent tasks compete for limited resources (e.g., CPU cycles, memory bandwidth, disk I/O), significantly increasing response times and failing to meet real-time requirements. 3. Inefficient Resource Utilization: Single-server systems may experience resource under-utilization due to static resource allocation strategies, even during periods of high task demand. This results from the inability to dynamically adjust resource distribution based on real-time workloads, wasting available computational capacity. The classical token bucket algorithm, despite its simplicity, relies on static parameters that fail to adapt to the dynamic complexity of GIS tasks, leading to suboptimal performance under high concurrency. By contrast, our proposed method addresses this limitation through three key innovations: multi-dimensional load assessment, queueing theory-driven dynamic rate adjustment, and HMM-based predictive traffic shaping. The increasing demand for real-time spatio-temporal data services in single-server GIS platforms underscores the critical need for adaptive traffic-shaping mechanisms to address resource limitations and workload heterogeneity. Traditional token bucket algorithms, while effective for homogeneous network traffic, struggle to dynamically adjust to GIS tasks' varying complexity (e.g., simple queries vs. computationally intensive downloads) and  
Figure 17: Average throughput for dynamic traffic scenarios. resource demands, leading to suboptimal performance under high concurrency. This study bridges this gap by introducing an adaptive framework that integrates multi-dimensional load assessment, queueing theory-driven rate optimization, and HMM-based predictive control, specifically tailored to single-server GIS environments. In contrast to multi-server load balancing approaches, which rely on hardware scaling, our method optimizes resource utilization within constrained single-server settings. By monitoring CPU, memory, and resource link utilization with adaptive weights, we capture the holistic system state, avoiding the single-metric bias inherent in classical algorithms. The M/M/1 queueing model dynamically computes the expected task arrival rate (\(\lambda\)) based on real-time load, enabling proactive token fill rate adjustments that align with system capacity. This addresses the classical algorithm's static parameter limitation, as demonstrated by the 9.56% latency reduction and 5.57% throughput improvement in dynamic traffic scenarios. The HMM-based predictive component further distinguishes our approach by modeling load-state transitions, allowing adaptive rate adjustments to anticipated workloads. This innovation is particularly critical for complex GIS tasks, where the fixed rates fail to mitigate latency fluctuations in mixed task scenarios and dynamic traffic scenarios.