高性能包分类技术及其应用研究
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摘要
随着计算机硬件体系结构和虚拟网络的迅猛发展，包分类(PacketClassification)的运行模式发生了显著的变化：首先，包分类的物理路由平台从单核处理器向多核处理器过渡；其次，一个物理路由器需要被复用为大量的虚拟路由器。本文针对包分类运行模式的变化，研究支持大规模规则库的高效多维包分类算法，以及在多核处理器路由平台上支持多虚拟路由器的高效包分类技术，最后将提出的算法应用到网络新型业务的流量识别中。
(1)支持大规模规则库的高效多维包分类算法包括：基于规则集压缩的多维包分类决策树研究和基于并行分布式联合布鲁姆过滤器的包分类算法。
基于规则集压缩的多维包分类决策树研究包括两个部分，首先提出基于规则集压缩的二维地址前缀匹配算法EGT-SC(Extended Grid of Triewith Sets Compression)。然后，针对新型业务的包分类规则库特点，提出在多维多模式匹配情况下的四种包分类决策树，通过实验比较这些决策树的时间和空间性能差异，进而得出适合新型业务包分类的最佳算法。
基于并行分布式联合布鲁姆过滤器的包分类算法PDCBF(ParallelDistributed Combination Bloom Filter)分为两个阶段：规则存储阶段和包匹配阶段，两个阶段的核心部件都是聚合结点。在规则存储阶段，算法将所有规则依据协议存放到三个匹配逻辑单元中，相对原始数据包匹配规则数目来说，每个逻辑单元的待匹配规则数目大大减少。包匹配阶段分为三个步骤，协议匹配、地址的单域匹配和聚合匹配。理论分析和实验结果表明，PDCBF算法包匹配效率高、硬件资源消耗合理、包分类准确率高，是一种支持大规模规则库的高效多维包分类算法。
(2)在多核处理器路由平台上支持多虚拟路由器的包分类技术包括基于演化博弈（非合作博弈）和基于联盟博弈（合作博弈）的多虚拟路由器的多核动态选择算法。
非合作博弈方面，通过多个独立群体的演化博弈分析和建模多虚拟路由器的多核动态选择行为，演化均衡的策略分布作为最后的解。本文基于演化博弈模型提出了三种演化算法：群体演化算法、强化学习算法和基于均衡迭代方程的分布式演化算法。实验结果表明，三种算法都能够实现多核的负载均衡，保障各个虚拟路由器公平地使用计算资源，并且维持系统稳定的高吞吐量。基于均衡迭代方程的分布式演化算法比群体演化算法和强化学习算法具有更快的收敛速度。
合作博弈方面，本文针对多虚拟路由器的多核动态选择问题建立联盟博弈模型，通过联盟形成的过程设计了一个多核选择算法，即核联盟算法。算法中，核与核之间组成联盟，每个核可以属于不同的联盟，由一个联盟组成的集合为虚拟路由器提供计算服务。实验结果表明，算法可以有效地实现多核的负载均衡和维持系统稳定的高吞吐量。
(3)新型业务的流量识别技术包括两个部分：基于H.323协议的VoIP语音流量识别技术和一种高效的P2P流量识别技术。
本文通过分析基于H.323协议的VoIP语音通信过程中出现的会话特征，提取出通信方的元组信息，进而识别整个语音会话流量。同时，设计相应流结点的存储、搜索和更新方案，提出一种H.323语音流量的识别算法。仿真实验结果表明，与传统流量识别方法相比，本文算法能够更加准确地识别基于H.323协议的VoIP语音流量。
针对目前网络中典型的P2P应用，本文通过分析通信终端会话过程中的特征，提取数据传输通道的五元组信息，建立P2P流量的识别规则库，并且设计相应的流存储结构和提出一种识别P2P流量的高效包分类算法。仿真实验和实际的链路测试结果表明，与基于端口的识别方法和基于行为特征的识别方法相比，本文算法具有更高的识别精度和更快的识别速度，可以有效地进行多种协议的P2P应用的流量识别，具有实际应用价值。
With the rapid development of computer hardware architecture and virtual network, the operational mode of packet classification has changed dramatically. First, the physical routing platform of packet classification has been transforming from single-core processors to multi-core processors. Second, a physical router needs to be multiplexed as a large number of virtual routers. In order to solve the problems brought by the changing operational mode of packet classification, this paper focuses on efficient multi-dimensional packet classification algorithms which can support large-scale rule sets, and efficient packet classification technology which can support multiple virtual routers on a multi-core processor routing platform. Moreover, the new proposed algorithms in this paper are applied to traffic identification of new network services.
(1) This paper proposes two efficient multi-dimensional packet classification algorithms supporting large-scale rule sets. They are respectively the multi-dimensional packet classification decision trees based on sets compression and the packet classification algorithm based on parallel distributed combination bloom filters.
The study of multi-dimensional packet classification decision trees based on sets compression consists of two parts. First, a two-dimensional address prefix matching algorithm based on sets compression EGT-SC (Extended Grid of the Trie with Sets Compression) is firstly proposed. Then according to the characteristics of rule sets in new services, four packet classification decision trees applied to multi-dimensional multi-mode matching are presented. Finally, several experiments are carried out to compare time performance and space performance of the four decision trees and the most suitable packet classification algorithm to new services is found.
The packet classification algorithm based on parallel distributed combination bloom filter PDCBF (Parallel Distributed Combination Bloom Filter) is divided into two stages. They are the stage of rules storage and the stage of packet matching. The core components of the two stages are the aggregation nodes. In the stage of rules storage, the algorithm divides all the original rule sets into three matching logical units based on protocol. Compared with the numbers of the original packet matching rules, the scale of rule sets for packet matching is greatly reduced. In the stage of packet matching, there are three matching steps, and they are protocol matching, address single-field matching and aggregation matching. The theoretical analysis and experimental results show that PDCBF algorithm has the features of high-efficient packet matching, reasonable consumption of hardware resources and high-accurate packet classification. And it is an efficient multi-dimensional packet classification algorithm for large-scale rule sets.
(2) The packet classification techniques supporting multiple virtual routers on the multi-core processor routing platform include two multi-core dynamic selection algorithms for multiple virtual routers, and they are respectively based on evolutionary game (non-cooperative game) and coalitional game (cooperative game).
The multi-core dynamic selection behaviors of multiple virtual routers are analyzed and modeled by multiple independent groups in evolutionary game. The strategy distribution of evolutionary equilibrium is the final solution. This paper proposes three evolutionary algorithms based on evolutionary game model. The three evolutionary algorithms are respectively the population evolution algorithm, the reinforcement learning algorithm and the distributed evolutionary algorithm. The experimental results show that the three algorithms are able to achieve balanced loads among the cores, guarantee fair use of computing resources among virtual routers and maintain stable high-throughput of the system. Distributed evolutionary algorithm based on the balanced iterative equation has faster convergence speed than the other two algorithms.
This paper also constructs a coalitional game model to solve the problem of multi-core dynamic selection for multiple virtual routers. Then an efficient multiprocessor selection scheme based on the process of coalition formation is presented. In this algorithm, it is the cores that constitute coalitions in order to increase the throughput performance of the system. A core can belong to different coalitions, and a whole coalition services for virtual routers. The simulation results demonstrate that our algorithm can effectively support load balance and maintain stable high-throughputs of the system.
(3) This paper proposes two traffic identification techniques for new network services. They are respectively a H.323-based VoIP traffic identification technique and an efficient P2P traffic identification technique.
This paper firstly analyzes the H.323protocol and the session characteristics in the communication process. By extracting tuple information of the communication participant, the whole VoIP traffic can be identified. Then a scheme of storage, search and update for traffic nodes is designed and a H.323-based VoIP traffic identification algorithm is proposed. The simulation results show that our algorithm can identify the H.323-based VoIP voice traffic more accurately, compared with the traditional traffic identification method.
For the typical P2P applications in the network, this paper extracts the five-tuple information in the data transmission channel by analyzing the characteristics of communication terminal session. Then a rule set for identifying P2P traffic is built and an efficient packet classification algorithm for identifying P2P traffic is proposed. The simulation and actual link testing results show that compared with the port-based and the host-behavior-based identification methods, the proposed algorithm has higher identification accuracy and faster identification speed. And it can effectively identify P2P application traffic of multiple protocols and is of more practical value.
(1)支持大规模规则库的高效多维包分类算法包括：基于规则集压缩的多维包分类决策树研究和基于并行分布式联合布鲁姆过滤器的包分类算法。
基于规则集压缩的多维包分类决策树研究包括两个部分，首先提出基于规则集压缩的二维地址前缀匹配算法EGT-SC(Extended Grid of Triewith Sets Compression)。然后，针对新型业务的包分类规则库特点，提出在多维多模式匹配情况下的四种包分类决策树，通过实验比较这些决策树的时间和空间性能差异，进而得出适合新型业务包分类的最佳算法。
基于并行分布式联合布鲁姆过滤器的包分类算法PDCBF(ParallelDistributed Combination Bloom Filter)分为两个阶段：规则存储阶段和包匹配阶段，两个阶段的核心部件都是聚合结点。在规则存储阶段，算法将所有规则依据协议存放到三个匹配逻辑单元中，相对原始数据包匹配规则数目来说，每个逻辑单元的待匹配规则数目大大减少。包匹配阶段分为三个步骤，协议匹配、地址的单域匹配和聚合匹配。理论分析和实验结果表明，PDCBF算法包匹配效率高、硬件资源消耗合理、包分类准确率高，是一种支持大规模规则库的高效多维包分类算法。
(2)在多核处理器路由平台上支持多虚拟路由器的包分类技术包括基于演化博弈（非合作博弈）和基于联盟博弈（合作博弈）的多虚拟路由器的多核动态选择算法。
非合作博弈方面，通过多个独立群体的演化博弈分析和建模多虚拟路由器的多核动态选择行为，演化均衡的策略分布作为最后的解。本文基于演化博弈模型提出了三种演化算法：群体演化算法、强化学习算法和基于均衡迭代方程的分布式演化算法。实验结果表明，三种算法都能够实现多核的负载均衡，保障各个虚拟路由器公平地使用计算资源，并且维持系统稳定的高吞吐量。基于均衡迭代方程的分布式演化算法比群体演化算法和强化学习算法具有更快的收敛速度。
合作博弈方面，本文针对多虚拟路由器的多核动态选择问题建立联盟博弈模型，通过联盟形成的过程设计了一个多核选择算法，即核联盟算法。算法中，核与核之间组成联盟，每个核可以属于不同的联盟，由一个联盟组成的集合为虚拟路由器提供计算服务。实验结果表明，算法可以有效地实现多核的负载均衡和维持系统稳定的高吞吐量。
(3)新型业务的流量识别技术包括两个部分：基于H.323协议的VoIP语音流量识别技术和一种高效的P2P流量识别技术。
本文通过分析基于H.323协议的VoIP语音通信过程中出现的会话特征，提取出通信方的元组信息，进而识别整个语音会话流量。同时，设计相应流结点的存储、搜索和更新方案，提出一种H.323语音流量的识别算法。仿真实验结果表明，与传统流量识别方法相比，本文算法能够更加准确地识别基于H.323协议的VoIP语音流量。
针对目前网络中典型的P2P应用，本文通过分析通信终端会话过程中的特征，提取数据传输通道的五元组信息，建立P2P流量的识别规则库，并且设计相应的流存储结构和提出一种识别P2P流量的高效包分类算法。仿真实验和实际的链路测试结果表明，与基于端口的识别方法和基于行为特征的识别方法相比，本文算法具有更高的识别精度和更快的识别速度，可以有效地进行多种协议的P2P应用的流量识别，具有实际应用价值。
With the rapid development of computer hardware architecture and virtual network, the operational mode of packet classification has changed dramatically. First, the physical routing platform of packet classification has been transforming from single-core processors to multi-core processors. Second, a physical router needs to be multiplexed as a large number of virtual routers. In order to solve the problems brought by the changing operational mode of packet classification, this paper focuses on efficient multi-dimensional packet classification algorithms which can support large-scale rule sets, and efficient packet classification technology which can support multiple virtual routers on a multi-core processor routing platform. Moreover, the new proposed algorithms in this paper are applied to traffic identification of new network services.
(1) This paper proposes two efficient multi-dimensional packet classification algorithms supporting large-scale rule sets. They are respectively the multi-dimensional packet classification decision trees based on sets compression and the packet classification algorithm based on parallel distributed combination bloom filters.
The study of multi-dimensional packet classification decision trees based on sets compression consists of two parts. First, a two-dimensional address prefix matching algorithm based on sets compression EGT-SC (Extended Grid of the Trie with Sets Compression) is firstly proposed. Then according to the characteristics of rule sets in new services, four packet classification decision trees applied to multi-dimensional multi-mode matching are presented. Finally, several experiments are carried out to compare time performance and space performance of the four decision trees and the most suitable packet classification algorithm to new services is found.
The packet classification algorithm based on parallel distributed combination bloom filter PDCBF (Parallel Distributed Combination Bloom Filter) is divided into two stages. They are the stage of rules storage and the stage of packet matching. The core components of the two stages are the aggregation nodes. In the stage of rules storage, the algorithm divides all the original rule sets into three matching logical units based on protocol. Compared with the numbers of the original packet matching rules, the scale of rule sets for packet matching is greatly reduced. In the stage of packet matching, there are three matching steps, and they are protocol matching, address single-field matching and aggregation matching. The theoretical analysis and experimental results show that PDCBF algorithm has the features of high-efficient packet matching, reasonable consumption of hardware resources and high-accurate packet classification. And it is an efficient multi-dimensional packet classification algorithm for large-scale rule sets.
(2) The packet classification techniques supporting multiple virtual routers on the multi-core processor routing platform include two multi-core dynamic selection algorithms for multiple virtual routers, and they are respectively based on evolutionary game (non-cooperative game) and coalitional game (cooperative game).
The multi-core dynamic selection behaviors of multiple virtual routers are analyzed and modeled by multiple independent groups in evolutionary game. The strategy distribution of evolutionary equilibrium is the final solution. This paper proposes three evolutionary algorithms based on evolutionary game model. The three evolutionary algorithms are respectively the population evolution algorithm, the reinforcement learning algorithm and the distributed evolutionary algorithm. The experimental results show that the three algorithms are able to achieve balanced loads among the cores, guarantee fair use of computing resources among virtual routers and maintain stable high-throughput of the system. Distributed evolutionary algorithm based on the balanced iterative equation has faster convergence speed than the other two algorithms.
This paper also constructs a coalitional game model to solve the problem of multi-core dynamic selection for multiple virtual routers. Then an efficient multiprocessor selection scheme based on the process of coalition formation is presented. In this algorithm, it is the cores that constitute coalitions in order to increase the throughput performance of the system. A core can belong to different coalitions, and a whole coalition services for virtual routers. The simulation results demonstrate that our algorithm can effectively support load balance and maintain stable high-throughputs of the system.
(3) This paper proposes two traffic identification techniques for new network services. They are respectively a H.323-based VoIP traffic identification technique and an efficient P2P traffic identification technique.
This paper firstly analyzes the H.323protocol and the session characteristics in the communication process. By extracting tuple information of the communication participant, the whole VoIP traffic can be identified. Then a scheme of storage, search and update for traffic nodes is designed and a H.323-based VoIP traffic identification algorithm is proposed. The simulation results show that our algorithm can identify the H.323-based VoIP voice traffic more accurately, compared with the traditional traffic identification method.
For the typical P2P applications in the network, this paper extracts the five-tuple information in the data transmission channel by analyzing the characteristics of communication terminal session. Then a rule set for identifying P2P traffic is built and an efficient packet classification algorithm for identifying P2P traffic is proposed. The simulation and actual link testing results show that compared with the port-based and the host-behavior-based identification methods, the proposed algorithm has higher identification accuracy and faster identification speed. And it can effectively identify P2P application traffic of multiple protocols and is of more practical value.
引文
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