Adaptive Batch Update in TCAM: How Collective Optimization Beats Individual Ones

Abstract

Rule update in TCAM has long been identified as a key technical challenge due to the rule order constraint. Existing algorithms take each rule update as an independent task. However, emerging applications produce batch rule update requests. Processing the updates individually causes high aggregated cost which can strain the processor and/or incur excessive TCAM lookup interrupts. This paper presents the first true batch update algorithm, ABUT. Unlike the other alleged batch update algorithms, ABUT collectively evaluates and optimizes the TCAM placement for whole batches throughout. By applying the topology grouping and maintaining the group order invariance in TCAM, ABUT achieves substantial computing time reduction yet still yields the best-in-class placement cost. Our evaluations show that ABUT is ideal for low-latency and high-throughput batch TCAM updates in modern high-performance switches.