## 6 Conclusions  
The adaptive token bucket algorithm proposed in this paper optimizes system performance under varying task types and concurrency levels by dynamically adjusting the token fill rate. Compared to the classic token bucket algorithm, the proposed method effectively reduces the average processing delay in different task scenarios, while improving the system's throughput under high-concurrency conditions. Notably, the proposed method demonstrates superior processing capability when the concurrency is high, further validating the effectiveness and advantages of the adaptive adjustment mechanism. Overall, the proposed method has good adaptability in improving system throughput and reducing latency, providing a more flexible and efficient solution for traffic shaping and system load optimization. The following conclusions are drawn:  
1. Existing methods use a fixed token fill rate for traffic shaping, but the optimal rate is difficult to determine. The proposed method adapts the token fill rate based on system load. In terms of average latency, for different task scenarios, under highly concurrent conditions, the average latency of the proposed method is almost lower than that of the classic token bucket algorithm with the optimal rate; the proposed method can effectively reduce the average latency. For simple task scenarios, when the concurrency is 185, the average latency of the classic token bucket algorithm is 1.37 times that of the proposed method. For general task scenarios, when the concurrency exceeds 150, the average latency of the proposed method is roughly the same as that of the classic token bucket algorithm. For complex task scenarios, when the concurrency exceeds 120, the average latency of the classic token bucket algorithm is higher than that of the proposed method, with a maximum difference of 1.05 times. For mixed task scenarios, at a concurrency level of 130, the classic token bucket algorithm's average latency is 1.08 times that of the proposed method. For dynamic traffic scenarios, the proposed method reduces the average latency by approximately 9.56%.  
In terms of average throughput, the proposed method can improve the processing efficiency under highly concurrent and dynamic traffic conditions, especially in complex task scenarios, mixed task scenarios and dynamic traffic scenarios. For complex task scenarios, when the concurrency reaches 155, the average throughput of the proposed method exceeds that of the classic token bucket algorithm, with the maximum improvement reaching 6 TPS. For mixed task scenarios, at a concurrency level of 130, the proposed method shows an average throughput that is 8 TPS higher than that of the classic token bucket algorithm. For dynamic traffic scenarios, the average throughput of the proposed method is 5.57% higher than that of the classic token bucket algorithm.  
Conceptualization, [PERSON] ([PERSON]) and P.W.; methodology, [PERSON] ([PERSON]) and H.Z.; software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), H.Z. and [PERSON]. ([PERSON]); formal analysis, [PERSON] and P.W.; investigation, [PERSON] ([PERSON]); resources, [PERSON] and Z.M.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and H.Z.; visualization, [PERSON] ([PERSON]) and [PERSON]; supervision, [PERSON] ([PERSON]); project administration, Z.M.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2024 YFB2505800, and the Fundamental Research Funds for Central Public Welfare Research Institutes, grant number AR2504.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.