Fast and scalable layer four switching
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Packet classification on multiple fields
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Proceedings of the 40th annual Design Automation Conference
Packet classification using multidimensional cutting
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Packet classification in large ISPs: design and evaluation of decision tree classifiers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
FlexPath NP: a network processor concept with application-driven flexible processing paths
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Journal of Systems Architecture: the EUROMICRO Journal
A high-performance architecture and BDD-based synthesis methodology for packet classification
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Algorithms for packet classification
IEEE Network: The Magazine of Global Internetworking
An application-aware load balancing strategy for network processors
HiPEAC'10 Proceedings of the 5th international conference on High Performance Embedded Architectures and Compilers
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Multi-field packet classification problems discussed in the literature are typically constrained to the Internet five-tuple and primarily address the problem of network quality-of-service (QoS) support and access control. In this paper, we present a solution for a classification problem that is used for optimized packet assignment to different data paths within a network processor system-on-chip (SoC). In contrast to the five-tuple-based rules discussed in the prior art, our problem has rules that consider a larger set of fields from the packet header. However, for each individual rule a different sub-set of fields is relevant and the number of rules is smaller. Based on a specification of the usage case for our classifier we derive heterogeneous decision graph algorithm (HDGA), a heuristic approach to construct a decision tree classifier that integrates external lookup results for certain types of rules. We evaluate various parameters for optimizing the proposed decision tree and present simulation results to show the scalability of HDGA for typical problem sizes. This paper is concluded with the results of an implementation on our field-programmable gate-array (FPGA)-based prototyping platform.