Machine Learning
Network Processors
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Firewall Design: Consistency, Completeness, and Compactness
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Survey and taxonomy of packet classification techniques
ACM Computing Surveys (CSUR)
Packet classifiers in ternary CAMs can be smaller
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
In VINI veritas: realistic and controlled network experimentation
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
On the Safety and Efficiency of Firewall Policy Deployment
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
Compressing rectilinear pictures and minimizing access control lists
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient IP-address lookup with a shared forwarding table for multiple virtual routers
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Towards systematic design of enterprise networks
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Topological transformation approaches to optimizing TCAM-based packet classification systems
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
| Hi-index | 0.00 |
Multiple packet filters serving different purposes (e.g., firewalling, QoS) and different virtual routers are often deployed on a single physical router. The HyperCuts decision tree is one efficient data structure for performing packet filter matching in software. Constructing a separate HyperCuts decision tree for each packet filter is not memory efficient. A natural alternative is to construct shared HyperCuts decision trees to more efficiently support multiple packet filters. However, we experimentally show that naively classifying packet filters into shared HyperCuts decision trees may significantly increase the memory consumption and the height of the trees. To help decide which subset of packet filters should share a HyperCuts decision tree, we first identify a number of important factors that collectively impact the efficiency of the resulted shared HyperCuts decision tree. Based on the identified factors, we then propose to use machine learning techniques to predict whether any pair of packet filters should share a tree. Given the pair-wise prediction matrix, a greedy heuristic algorithm is used to classify packets filters into a number of shared HyperCuts decision trees. Our experiments using both real packets filters and synthetic packet filters show that the shared HyperCuts decision trees consume considerably less memory.