Name
Abstract | ||
|---|---|---|
Packet classification is a fundamental enabling function for various applications in switches, routers and firewalls. Due to their performance and scalability limitations, current packet classification solutions are insufficient in ad-dressing the challenges from the growing network bandwidth and the increasing number of new applications. This paper presents a scalable parallel architecture, named Para Split, for high-performance packet classification. We propose a rule set partitioning algorithm based on range-point conversion to reduce the overall memory requirement. We further optimize the partitioning by applying the Simulated Annealing technique. We implement the architecture on a Field Programmable Gate Array (FPGA) to achieve high throughput by exploiting the abundant parallelism in the hardware. Evaluation using real-life data sets including Open Flow-like 11-tuple rules shows that Para Split achieves significant reduction in memory requirement, compared with the-state-of-the-art algorithms such as Hyper Split [6] and EffiCuts [8]. Because of the memory efficiency of Para Split, our FPGA design can support in the on-chip memory multiple engines, each of which contains up to 10K complex rules. As a result, the architecture with multiple Para Split engines in parallel can achieve up to Terabit per second throughput for large and complex rule sets on a single FPGA device. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/HOTI.2012.17 | Hot Interconnects |
Keywords | DocType | ISBN |
scalable architecture,terabit packet classification,fpga design,on-chip memory,multiple para split engine,para split,memory requirement,hyper split,current packet classification solution,high-performance packet classification,overall memory requirement,memory efficiency,simulated annealing,terabit,clustering algorithms,field programmable gate arrays,decision trees,openflow,throughput,fpga | Conference | 978-1-4673-2836-4 |
Citations | PageRank | References |
23 | 1.07 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Fong | 1 | 68 | 3.35 |
| Xiang Wang | 2 | 51 | 5.71 |
| Yaxuan Qi | 3 | 144 | 14.33 |
| Jun Li | 4 | 338 | 38.15 |
| Weirong Jiang | 5 | 521 | 32.11 |