Fair Adaptive Bandwidth Allocation: A Rate Control Based Active Queue Management Discipline
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We propose Fair Adaptive Bandwidth Allocation (FABA), a buffer management discipline that ensures a fair bandwidth allocation amongst competing flows even in the presence of non-adaptive traffic. FABA is a rate control based active queue management discipline that provides explicit fairness and can be used to partition bandwidth in proportion to pre-assigned weights. FABA is well-suited for allocation of bandwidth to aggregate flows as required in the differentiated services framework. We study and compare FABA with other well known queue management disciplines and show that FABA ensures fair allocation of bandwidth across a much wider range of buffer sizes at a bottleneck router. FABA uses randomization and has an O(1) average time complexity, and, is therefore scalable. The space complexity of the proposed algorithm is O(B) where B is the buffer size at the bottleneck router. We argue that though FABA maintains per active-flow state, through O(1) computation, reasonably scalable implementations can be deployed which is sufficient for network edges and ISPs.