FROD: An Efficient Framework for Optimizing Decision Trees in Packet Classification | IEEE Conference Publication | IEEE Xplore

FROD: An Efficient Framework for Optimizing Decision Trees in Packet Classification


Abstract:

To perform efficient packet classification, decision tree-based methods conduct decision trees via hand-tuned heuristics. Then the performance testing and optimization ar...Show More

Abstract:

To perform efficient packet classification, decision tree-based methods conduct decision trees via hand-tuned heuristics. Then the performance testing and optimization are executed to ensure an excellent searching speed and space overhead. Specifically, when the performance is below expectation, existing solutions attempt to optimize the algorithms, such as conducting more sophisticated heuristics. However, reconstruction or adjustment for algorithms produces an intolerable time overhead due to the long optimization period, caused by uncertain performance benefits and high pre-processing time. In this paper, we propose FROD, an efficient framework for optimizing the decision trees directly in packet classification. FROD raises a meticulous evaluation to accurately appraise decision trees constructed by different heuristics. It then seeks out the bottleneck components via a lightweight heuristic. After that, FROD searches the optimal division for inferior components considering structural constraints and characteristics of traffic distribution. Evaluation on ClassBench shows that FROD benefits existing decision tree-based solutions in classification time by 41% and memory footprint by 19% on average, and reduces classification time by up to 64%.
Date of Conference: 10-12 June 2022
Date Added to IEEE Xplore: 05 July 2022
ISBN Information:
Print on Demand(PoD) ISSN: 1548-615X
Conference Location: Oslo, Norway

Funding Agency:


References

References is not available for this document.