PFTB: A Prediction-Based Fair Token Bucket Algorithm based on CRDT

In today’s rapidly evolving network landscape, an increasing number of applications are finding deployment on cloud-based computing platforms. With network traffic growing at an accelerated pace, the rational control of bandwidth utilization by these applications has emerged as a formidable technical challenge. Existing distributed rate limiting algorithms, while capable of enforcing stringent rate limits, often come at the cost of significant bandwidth wastage and lack comprehensive discussions on the global fairness of applications. In response to these challenges, we introduce an innovative distributed rate limiting algorithm termed the Prediction-based Fair Token Bucket, and it achieves equitable rate limiting among applications while optimizing the utilization of the entire network’s capacity. We introduce a TK-CRDT module based on a fair token bucket mechanism, which is integrated into our rate limiting algorithm. Through a comparative analysis against state-of-the-art rate limiting schemes, our algorithm enhances the excessive rate limiting metric by 91% and increases the Jain’s fairness index by 39%.