On using content addressable memory for packet classification

Packet switched networks such as the Internet require packet classification at every hop in order to ap-ply services and security policies to traffic flows. The relentless increase in link speeds and traffic volume imposes astringent constraints on packet classification solutions. Ternary Content Addressable Memory (TCAM) devices are favored by most network component and equipment vendors due to the fast and de-terministic lookup performance afforded by their use of massive parallelism. While able to keep up with high speed links, TCAMs suffer from exorbitant power consumption, poor scalability to longer search keys and larger filter sets, and inefficient support of multiple matches. The research community has responded with algorithms that seek to meet the lookup rate constraint with greater efficiency through the use of com-modity Random Access Memory (RAM) technology. The most promising algorithms efficiently achieve high lookup rates by leveraging the statistical structure of real filter sets. Due to their dependence on filter set characteristics, it is difficult to provision processing and memory resources for implementations that support a wide variety of filter sets. We show how several algorithmic advances may be leveraged to im-prove the efficiency, scalability, incremental update and multiple match performance of CAM-based packet classification techniques without degrading the lookup performance. Our approach, Label Encoded Content Addressable Memory (LECAM), represents a hybrid technique that utilizes decomposition, label encoding, and a novel Content Addressable Memory (CAM) architecture. By reducing the number of implementation parameters, LECAM provides a vehicle to carry several of the recent algorithmic advances into practice. We provide a thorough overview of CAM technologies and packet classification algorithms, along with a detailed discussion of the scaling issues that arise with longer search keys and larger filter sets. We also provide a comparative analysis of LECAM and standard TCAM using a collection of real and synthetic filter sets of various sizes and compositions

Efficient binary cutting packet classification

Packet classification is the process of distributing packets into ‘flows’ in an internet router. Router processes all packets which belong to predefined rule sets in similar manner& classify them to decide upon what all services packet should receive. It plays an important role in both edge and core routers to provideadvanced network service such as quality of service, firewalls and intrusion detection. These services require the ability to categorize & isolate packet traffic in different flows for proper processing. Packet classification remains a classical problem, even though lots of researcher working on the problem. Existing algorithms such asHyperCuts,boundary cutting and HiCuts have achieved an efficient performance by representing rules in geometrical method in a classifier and searching for a geometric subspace to which each inputpacket belongs. Some fixed interval-based cutting not relating to the actual space that eachrule covers is ineffective and results in a huge storage requirement. However, the memoryconsumption of these algorithms remains quite high when high throughput is required.Hence in this paper we are proposing a new efficient splitting criterion which is memory andtime efficient as compared to other mentioned techniques. Our proposed approach known as (ABC) Adaptive Binary Cuttingproducesa set of different-sized cuts at each decision step, with the goal to balance the distribution offilters and to reduce the filter duplication effect. The proposed algorithmuses stronger andmore straightforward criteria for decision treeconstruction. Experimental results will showthe effectiveness of proposed algorithm as compared to existing algorithm using differentparameters such as time & memory. In this paper, no symmetrical size cut at each decision node, with aim to make a distribution of filters balanced and also to reduce redundancy in filter

Comparative Evaluation of Packet Classification Algorithms for Implementation on Resource Constrained Systems

This paper provides a comparative evaluation of a number of known classification algorithms that have been considered for both software and hardware implementation. Differently from other sources, the comparison has been carried out on implementations based on the same principles and design choices. Performance measurements are obtained by feeding the implemented classifiers with various traffic traces in the same test scenario. The comparison also takes into account implementation feasibility of the considered algorithms in resource constrained systems (e.g. embedded processors on special purpose network platforms). In particular, the comparison focuses on achieving a good compromise between performance, memory usage, flexibility and code portability to different target platforms