HaRP: Rapid Packet Classification via Hashing Round-Down Prefixes

2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)

et al. 2015

Due to the rapid growth of Internet, there is an increasing need for efficiently classifying packets with many header fields in large rule sets. For example, in Software Defined Networking (SDN), the OpenFlow table lookup can require 15 packet header fields to be examined. In this paper, we present several decomposition-based packet classification implementations with efficient optimization techniques. In the searching phase, packet headers are split or combined. In the merging phase, the partial searching results from all the fields are merged to generate the final result. We prototype our implementations on state-of-the

Section: Platformsmentioning

confidence: 99%

Optimizing many-field packet classification on FPGA, multi-core general purpose processor, and GPU

Zhang

2015 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)

et al. 2015

“…The performance of a decision-tree-based approach highly relies on the complexity of the rule set. The memory consumption depends on the data sets; this can possibly lead to exponential rule set expansion [25]. Also, since the decision-tree is usually not balanced, the processing latency for packet classification is indeterministic.…”

Section: Decision-tree-based Approaches On Multi-core Gppsmentioning

confidence: 99%

“…The processing latency for each packet is measured by dividing the total processing latency by all the packets processed. 4 In [25], a decision-tree-based solution using hashing is proposed on a 4-core 2.8 GHz AMD Opteron system. It supports 5-field 30 K rule sets.…”

Section: Decision-tree-based Approaches On Multi-core Gppsmentioning

confidence: 99%

A Decomposition-Based Approach for Scalable Many-Field Packet Classification on Multi-core Processors

Prasanna

2014

Int J Parallel Prog

As a kernel function in network routers, packet classification requires the incoming packet headers to be checked against a set of predefined rules. There are two trends for packet classification: (1) to examine a large number of packet header fields, and (2) to use software-based solutions on multi-core general purpose processors and virtual machines. Although packet classification has been widely studied, most existing solutions on multi-core systems target the classic 5-field packet classification; it is not easy to scale up their performance with respect to the number of packet header fields. In this work, we present a decomposition-based packet classification approach; it supports large rule sets consisting of a large number of packet header fields. In our approach, range-tree and hashing are used to search the fields of the input packet header in parallel. The partial results from all the fields are represented in rule ID sets; they are merged efficiently to produce the final match result. We implement our approach and evaluate its performance with respect to overall throughput and processing latency for rule set size varying from 1 to 32 K. Experimental results on state-of-the-art 16-core platforms show that, an overall throughput of 48 million packets per second and a processing latency of 2,000 ns per packet can be achieved for a 32 K rule set.

“…A software-based router is implemented in [10] using HyperCuts on a 8-core Xeon processor. In [16], a decision-tree-based solution using hashing is proposed on a 4-core 2.8 GHz AMD Opteron system. However, the performance of a decision-tree-based approach is dependent on the statistical feature of the rule set as well as the shape of the tree; it is generally hard to model the performance mathematically.…”

Section: Related Workmentioning

confidence: 99%

“…Most prior work on multi-core processors target the classic 5-field packet classification (W = 5); in general, the decision-tree based approaches can achieve around 10 MPPS [14]- [16] on state-of-the-art multi-core processors. However, these approaches do not scale easily with respect to W : each cut in one field may lead to an exponential increase in the number of subspaces in other fields, therefore the latency and throughput performance deteriorate quickly as W increases.…”

Section: F Comparison With Prior Workmentioning

confidence: 99%

Performance modeling and optimizations for decomposition-based large-scale packet classification on multi-core processors

2014 IEEE 15th International Conference on High Performance Switching and Routing (HPSR)

Prasanna

2014

Abstract-Large-scale packet classification such as OpenFlow table lookup in Software Defined Networking (SDN) is a key task performed at the Internet routers. However, the increasing size of the rule set and the increasing width of each individual rule make large-scale packet classification a challenging problem. In this paper, we present a decompositionbased approach for large-scale packet classification on multicore processors. We develop a model to predict the performance of the classification engine with respect to throughput and latency. This model involves the architectural parameters of the multi-core processors and the design requirements of packet classification. Based on this model, we employ optimization techniques such as grouping short fields in the search phase and early termination of the merge phase. The performance model can be applied to other generic multi-field classification problems as well. To evaluate the accuracy of the performance model, we implement a 15-field classification engine on stateof-the-art multi-core processors. Experimental results show that, the proposed model predicts the performance with less than ±10% error. For a 32 K 15-field rule set, the optimized decomposition-based approach achieves 2000 ns per packet latency and 33 Million Packets Per Second (MPPS) throughput (49% of the peak throughput). The peak performance assumes an ideal execution model that uses an optimized execution sequence and ignores memory access latency, data dependencies, and context switch overhead.