Adaptive batch update in TCAM: How collective optimization beats individual ones

Wan，Ying1; Song，Haoyu2; Xu，Yang3; Zhang，Chuwen1; Wang，Yi4,5; Liu，Bin1,5

10.1109/INFOCOM42981.2021.9488758

2021-05-10

0743-166X

978-1-6654-3131-6

Proceedings - IEEE INFOCOM

2021-May

1-10

10-13 May 2021

Vancouver, BC, Canada

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.

Scalability Conferences Prediction algorithms Topology Task analysis Optimization Strain

其他

英语

CPCI-S ; EI

Computer Science ; Engineering ; Telecommunications

Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000702210400092

20213210732871

Electrical Engineering, General:709

2-s2.0-85111945878

Scopus

1.Tsinghua University,China
2.Futurewei Technologies,United States
3.Fudan University,China
4.Southern University of Science and Technology,China
5.Peng Cheng Laboratory,China

Wan，Ying,Song，Haoyu,Xu，Yang,et al. Adaptive batch update in TCAM: How collective optimization beats individual ones[C],2021:1-10.