Neural packet classification
Explore this paper's citation graph
Summary
NeuroCuts is a deep reinforcement learning approach to solve the packet classification problem that uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective.
- Type
- preprint
- Published
- 2019-02-27
- Cited by
- 138
- References
- 72
- Access
- Open access
- OpenAlex
- https://openalex.org/W2925933452
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:67856539
Keywords
Computer science, Memory footprint, Heuristics, Reinforcement learning, Artificial intelligence
References
- Packet Classification using Hierarchical Intelligent Cuttings
- Playing Atari with Deep Reinforcement Learning
- Deep learning for detecting robotic grasps
- Fast and flexible: Parallel packet processing with GPUs and click
- Packet classification using tuple space search
- Adaptive Congestion Control for Unpredictable Cellular Networks
- Packet classification on multiple fields
- Discrete Bit Selection: Towards a Bit-Level Heuristic Framework for Multi-Dimensional Packet Classification
- The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
- SAX-PAC (Scalable And eXpressive PAcket Classification)
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
- Algorithms for packet classification
- Algorithms for advanced packet classification with ternary CAMs
- ClassBench: A Packet Classification Benchmark
- Packet classification using multidimensional cutting
- Speech recognition with deep recurrent neural networks
- Human-level control through deep reinforcement learning
- Non-negative graph embedding
- Packet Classification Algorithms: From Theory to Practice
- Optimization of Multitype Branching Processes
Cited by
- RL-Cache: Learning-Based Cache Admission for Content Delivery
- Solving System Problems with Machine Learning
- Deep Reinforcement Learning in System Optimization
- Verifying Deep-RL-Driven Systems
- Explaining Deep Learning-Based Networked Systems
- TabTree: A TSS-assisted Bit-selecting Tree Scheme for Packet Classification with Balanced Rule Mapping
- ML-Pushback: Machine Learning Based Pushback Defense Against DDoS
- A Computational Approach to Packet Classification
- Enhancing the performance of decision tree-based packet classification algorithms using CPU cluster
- Tuple Space Assisted Packet Classification With High Performance on Both Search and Update
- End-to-end deep reinforcement learning in computer systems
- Qd-tree: Learning Data Layouts for Big Data Analytics
- Multibit Tries Packet Classification with Deep Reinforcement Learning
- Interpreting Deep Learning-Based Networking Systems
- Study of the cost of measuring virtualized networks. (Etude du coût de mesure des réseaux virtualisés)
- Baking the ruleset: A heat propagation relaxation to packet classification
- Precise and Adaptable: Leveraging Deep Reinforcement Learning for GAP-based Multipath Scheduler
- Running Neural Networks on the NIC
- Taming the Wildcards: Towards Dependency-free Rule Caching with FreeCache
- Online Safety Assurance for Deep Reinforcement Learning