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A curated collection of packet classification algorithms and their papers (2018–2026), including CutSplit, PartitionSort, TupleMerge, TabTree, CutTSS, MultilayerTuple, DynamicTuple, HybridTSS, PT-Tree, DBTable, KSet, and TupleTree-Compress, plus the ClassBench / ClassBench-NG benchmark tools. Sorted by publication year (oldest to newest).
| # | Algorithm | Type | Venue | Year |
|---|---|---|---|---|
| 1 | CutSplit | Decision Tree (Cut + Split) | IEEE INFOCOM | 2018 |
| 2 | PartitionSort | Hybrid TSS + Decision Tree | IEEE/ACM ToN | 2018 |
| 3 | TupleMerge | Merged Tuple Space | IEEE/ACM ToN | 2019 |
| 4 | TabTree | TSS-assisted Bit-selecting Tree | ACM/IEEE ANCS | 2019 |
| 5 | CutTSS | Decision Tree + TSS Hybrid | IEEE JSAC | 2020 |
| 6 | MultilayerTuple | Multi-layer Tuple Space | IEEE ICCCN | 2021 |
| 7 | DynamicTuple | Dynamic Adaptive Tuple | Computer Networks | 2022 |
| 8 | HybridTSS | Coarse/Fine-grained TSS Hybrid | APNet (ACM) | 2022 |
| 9 | PT-Tree | Cascading Prefix Tuple Tree | IEEE/ACM ToN | 2024 |
| 10 | DBTable | Discriminative Bitset Hash Table | IEEE/ACM ToN | 2024 |
| 11 | KSet | Hierarchical Hash Multi-field | IEEE Access | 2025 |
| 12 | TupleTree-Compress | Compressed Hash-based Decision Tree | Computer Networks | 2026 |
| — | ClassBench / ClassBench-NG | 📦 Benchmark Tools | IEEE/ACM ToN · ANCS | 2007 / 2017 |
MultilayerTuple, DynamicTuple, PT-Tree, DBTable, and KSet are the five core classifiers integrated in the AMPS (Adaptive Multi-Classifier Packet System) framework.
| GitHub | https://github.com/wenjunpaper/CutSplit |
| Paper | CutSplit: A Decision-Tree Combining Cutting and Splitting for Scalable Packet Classification |
| Venue | IEEE INFOCOM 2018 |
| Authors | Wenjun Li, Xianfeng Li, Hui Li et al. |
| Links | IEEE Xplore · Project |
| GitHub | https://github.com/sorrachai/PartitonSort |
| Paper | A Sorted-Partitioning Approach to Fast and Scalable Dynamic Packet Classification |
| Venue | IEEE/ACM Transactions on Networking, 2018 |
| Authors | Sorrachai Yingchareonthawornchai, James Daly, Alex X. Liu et al. |
| Links | IEEE Xplore |
| GitHub | https://github.com/drjdaly/tuplemerge |
| Paper | TupleMerge: Fast Software Packet Processing for Online Packet Classification |
| Venue | IEEE/ACM Transactions on Networking, 2019 |
| Authors | James Daly, Valerio Bruschi, Leonardo Linguaglossa et al. |
| Links | IEEE Xplore |
| GitHub | https://github.com/wenjunpaper/TabTree |
| Paper | TabTree: A TSS-assisted Bit-selecting Tree Scheme for Packet Classification with Balanced Rule Mapping |
| Venue | ACM/IEEE ANCS 2019 |
| Authors | Wenjun Li, Tong Yang, Yeim-Kuan Chang et al. |
| Links | IEEE Xplore · Project |
| GitHub | https://github.com/wenjunpaper/CutTSS |
| Paper | Tuple Space Assisted Packet Classification With High Performance on Both Search and Update |
| Venue | IEEE Journal on Selected Areas in Communications (JSAC), 2020 |
| Authors | Wenjun Li, Tong Yang, Ori Rottenstreich et al. |
| Links | IEEE Xplore · Project |
| GitHub | https://github.com/zcy-ict/MultilayerTuple |
| GitHub (AMPS) | https://github.com/JiaChangGit/amps/tree/main/src |
| Paper | MultilayerTuple: A General, Scalable and High-performance Packet Classification Algorithm for Software Defined Network System |
| Venue | IEEE ICCCN 2021 |
| Authors | Chunyang Zhang, Gaogang Xie |
| Links | IEEE Xplore |
| GitHub | https://github.com/zcy-ict/DynamicTuple |
| GitHub (AMPS) | https://github.com/JiaChangGit/amps/tree/main/src |
| Paper | DynamicTuple: The Dynamic Adaptive Tuple for High-Performance Packet Classification |
| Venue | Computer Networks, 2022 |
| Authors | Chunyang Zhang, Gaogang Xie, Xin Wang |
| Links | ScienceDirect |
| GitHub | https://github.com/wenjunpaper/HybridTSS |
| Paper | HybridTSS: A Recursive Scheme Combining Coarse- and Fine-Grained Tuples for Packet Classification |
| Venue | APNet 2022 |
| Authors | Yuxi Liu, Yao Xin, Wenjun Li et al. |
| Links | ACM DL · Project |
| GitHub | https://github.com/lavline/PT-Tree |
| GitHub (AMPS) | https://github.com/JiaChangGit/amps/tree/main/src |
| Paper | PT-Tree: A Cascading Prefix Tuple Tree for Packet Classification in Dynamic Scenarios |
| Venue | IEEE/ACM Transactions on Networking, 2024 |
| Authors | Zhengyu Liao, Shiyou Qian, Zhonglong Zheng et al. |
| Links | IEEE Xplore · ACM DL |
| GitHub (AMPS) | https://github.com/JiaChangGit/amps/tree/main/src |
| Paper | DBTable: Leveraging Discriminative Bitsets for High-Performance Packet Classification |
| Venue | IEEE/ACM Transactions on Networking, 2024 |
| Authors | Zhengyu Liao, Shiyou Qian, Zhonglong Zheng et al. |
| Links | IEEE Xplore |
| GitHub (AMPS) | https://github.com/JiaChangGit/amps/tree/main/src |
| Paper | Efficient Hierarchical Hash-Based Multi-Field Packet Classification with Fast Update for Software Switches |
| Venue | IEEE Access, 2025 |
| Authors | Yeim-Kuan Chang, Yu-Hung Lin, Jia-Chang Chang et al. |
| Links | IEEE Xplore |
| GitHub | https://github.com/daveMmd/TupleTree-Compress/ |
| Paper | TupleTree-Compress: Accelerating Hash-Based Packet Classification via Extreme Node Compression |
| Venue | Computer Networks, 2026 |
| Authors | Jincheng Zhong, Tao Li, Gaofeng Lv et al. |
| Links | ScienceDirect |
| Base Algorithm | TupleTree — IEEE Transactions on Network and Service Management, 2023 · IEEE Xplore |
Standard benchmark tools for generating synthetic rule sets and packet traces used in packet classification research.
| ClassBench | ClassBench-NG | |
|---|---|---|
| GitHub (integrated) | https://github.com/JiaChangGit/classbench-packet-classification | same (includes ng subdirectory) |
| Official GitHub | (hosted at WUSTL) | https://github.com/classbench-ng/classbench-ng |
| Paper | ClassBench: A Packet Classification Benchmark | ClassBench-ng: Recasting ClassBench After a Decade of Network Evolution |
| Venue | IEEE/ACM Transactions on Networking, 2007 | ACM/IEEE ANCS 2017 |
| Authors | David E. Taylor, Jonathan S. Turner | J. Matoušek, G. Antichi, A. Lučanský et al. |
| Links | ACM DL | ResearchGate · Project |
ClassBench-NG extends the original (unmaintained) ClassBench with support for IPv6 and OpenFlow 1.0 rule generation, and improves IPv4 generation accuracy.
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