AGILE: Achieving Max-Min Fairness and High Utilization for In-Network Bandwidth Allocation

Abstract

Network operators utilize in-network bandwidth allocation strategies to ensure fairness among competing flows. Existing approaches mainly rely on two mechanisms deployed in switches: packet scheduling algorithms and probabilistic packet dropping. However, their effectiveness is constrained by the limited number of physical priority queues and the hardware overhead of arithmetic operations. The former leads to high packet loss, resulting in low link utilization, while the latter impairs bandwidth allocation accuracy.In this paper, we propose AGILE, a novel bandwidth allocation strategy that enhances network fairness while maintaining high link utilization. Despite the limited flow visibility in switches, AGILE performs real-time global flow state tracking and optimal per-flow fair share computation to achieve max-min fairness, dynamically allocating residual bandwidth to maximize link utilization. We implement a prototype of AGILE on Intel Tofino switches. Our experimental results show that AGILE attains an average Jain’s Fairness Index (JFI) of 0.996. Compared to state-of-the-art approaches, including Cebinae and AHAB, AGILE reduces relative bandwidth allocation errors by 45.7%–86.3%, improves link utilization by 5%, and decreases link utilization fluctuations by 37%–75%.