Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size
Saad Saleh, Sunny Shu, Boris Koldehofe · 2024
Active Queue Management (AQM) algorithms are heavily used in packet processors to maintain an optimal queue size and avoid issues like Bufferbloat. Despite the remarkable performance, the traditional AQM algorithms face a major challenge of estimating the accurate queue congestion due to bursty network conditions. The major reason is the use of baseline queue statistics for congestion estimation like delay and sojourn time for Random Early Detection (RED) and Controlled Delay (CoDel), respectively. In this paper, we propose a novel dAQM algorithm that uses advanced traffic statistics like three higher-order derivatives of sojourn time and buffer size along with the baseline sojourn time and buffer size for accurate congestion estimation. dAQM adjusts its drop rate based on the continuously varying congestion to cater to the needs of bursty traffic. We simulated dAQM in ns-3 and analyzed its performance for FTP traffic by variation in traffic load and packet sizes. The results showed that dAQM provides at least 25% and 39.7% reduction in packet loss ratio and flow completion time, respectively, as compared to the traditional AQM algorithms.