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Optimizing Multi-Dimensional packet classification for Multi-Core Systems

Journal of Computer Science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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High-Performance packet classification on Multi-Core Network Processing Platforms

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Tsinghua Science and Technology 2011年第4期16卷 432-439页

作者：亓亚烜薛一波李军 Department of Automation Tsinghua University Research Institute of Information Technology Tsinghua University Tsinghua National Laboratory for Information Science and Technology

packet classification is crucial to the implementation of advanced network services that require the capability to distinguish traffic in different flows, such as access control in firewalls and protocol analysis in intrusion detection systems. This paper proposes a novel packet classification algorithm optimized for multi-core network processors. The proposed algorithm, AggreCuts, has an explicit worst-case search time with modest memory usage. The data structure of AggreCuts is flexible and well-adapted to different types of multi-core platforms. The algorithm on both Intel IXP2850 32-bit and Cavium OCTEON3860 64-bit multi-core platforms was implemented to evaluate the performance of AggreCuts. The experimental results show that AggreCuts outperforms the best-known existing algorithm in terms of memory usage and classification speed.

关键词： packet classification multi-core performance evaluation

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A Fast packet classification Algorithm Based on Classifier’s Characteristic Applying to Multi-fields

A Fast Packet Classification Algorithm Based on Classifier’...

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2003 International Conference on Communication Technology

作者： Liqin Tian Chuang Lin Zhangxi Tan Department of Computer Science and Technology Tsinghua University

Performing classification quickly on multi- fields is known to be difficult, and has poor worst-case performance. In this paper, we present a solution to the problem of rapidly classifying packets. Ou

关键词： packet classification Network Processor

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Efficient pipelined flow classification for intelligent data processing in IoT

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Digital Communications and Networks 2022年第4期8卷 561-575页

作者： Seyed Navid Mousavi Fengping Chen Mahdi Abbasi Mohammad R.Khosravi Milad Rafiee Department of Computer Engineering Engineering FacultyBu-Ali Sina UniversityHamedanIran Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Telecommunications Group Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.

关键词： Efficiency Intelligent flow processing IoT packet classification Pipeline

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ERFC:An Enhanced Recursive Flow classification Algorithm

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Journal of Computer Science & Technology 2010年第5期25卷 958-969页

作者：龚向阳王文东程时端 State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications CCF

packet classification on multi-fields is a fundamental mechanism in network equipments,and various classification solutions have been proposed.Because of inherent difficulties,many of these solutions scale poorly in either time or space as rule sets grow in size.Recursive Flow classification（RFC） is an algorithm with a very high classifying speed. However,its preprocessing complexity and memory requirement are rather high.In this paper,we propose an enhanced RFC（ERFC） algorithm,in which a hash-based aggregated bit vector scheme is exploited to speed up its preprocessing procedure.A compressed and cacheable data structure is also introduced to decrease total memory requirement and improve its searching performance.Evaluation results show that ERFC provides a great improvement over RFC in both space requirement and preprocessing time.The search time complexity of ERFC is equivalent to that of RFC in the worst case; and its average classifying speed is improved by about 100%.

关键词： packet classification ERFC（enhanced recursive flow classification） preprocessing and storage optimization

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基于流标签的新型可降维IPv6报文分类算法(英文)

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China Communications 2012年第5期9卷 1-9页

作者：黄小红马严 Institute of Network Technology Beijing University of Posts and Telecommunications

Traditional packet classification for IPv4 involves examining standard 5-tuple of a packet header,source address,destination address,source port,destination port and protocol.With introduction of IPv6 flow label field which entails labeling the packets belonging to the same flow,packet classification can be resolved based on 3 dimensions:flow label,source address and destination address.In this paper,we propose a novel approach for the 3-tuple packet classification based on flow label.Besides,by introducing a conversion engine to covert the source-destination pairs to the compound address prefixes,we put forward an algorithm called Reducing Dimension(RD) with dimension reduction capability,which combines heuristic tree search with usage of buckets.And we also provide an improved version of RD,called Improved RD(IRD),which uses two mechanisms:path compression and priority tag,to optimize the performance.To evaluate our algorithm,extensive experiments have been conducted using a number of synthetically generated databases.For the memory consumption,the two proposed new algorithms only consumes around 3% of the existing algorithms when the number of filters increases to 10 k.And for the average search time,the search time of the two proposed algorithms is more than four times faster than others when the number of filters is 10 k.The results show that the proposed algorithm works well and outperforms many typical existing algorithms with the dimension reduction capability.

关键词： IPv6 packet classification flow label

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