Multilayer Packet Classification With Graphics Processing Units

Varvello, Matteo; Laufer, Rafael; Zhang, Feixiong; Lakshman, T. V.

doi:10.1109/tnet.2015.2491265

Cited by 47 publications

(42 citation statements)

References 21 publications

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“…These algorithms are not as popular as decision-tree based algorithms, because they are either too slow or consume too much memory. There are also solutions that exploit specialized hardware such as TCAMs, GPUs and FPGAs to support packet classification [17,24,32,33,42,48,50,57]. Compared to existing work, NeuroCuts is an algorithmic solution that applies Deep RL to generate efficient decision trees, with the capability to incorporate and improve on existing heuristics as needed.…”

Section: Related Workmentioning

confidence: 99%

Neural packet classification

Liang

Zhu

Jin

et al. 2019

Proceedings of the ACM Special Interest Group on Data Communication

106

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Packet classification is a fundamental problem in computer networking. This problem exposes a hard tradeoff between the computation and state complexity, which makes it particularly challenging. To navigate this tradeoff, existing solutions rely on complex hand-tuned heuristics, which are brittle and hard to optimize.In this paper, we propose a deep reinforcement learning (RL) approach to solve the packet classification problem. There are several characteristics that make this problem a good fit for Deep RL. First, many existing solutions iteratively build a decision tree by splitting nodes in the tree. Second, the effects of these actions (e.g., splitting nodes) can only be evaluated once the entire tree is built. These two characteristics are naturally captured by the ability of RL to take actions that have sparse and delayed rewards. Third, it is computationally efficient to generate data traces and evaluate decision trees, which alleviate the notoriously high sample complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective. It produces compact decision trees optimized for a specific set of rules and a given performance metric, such as classification time, memory footprint, or a combination of the two. Evaluation on ClassBench shows that NeuroCuts outperforms existing hand-crafted algorithms in classification time by 18% at the median, and reduces both classification time and memory footprint by up to 3×.

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Section: Related Workmentioning

confidence: 99%

Neural packet classification

Liang

Zhu

Jin

et al. 2019

Proceedings of the ACM Special Interest Group on Data Communication

106

View full text Add to dashboard Cite

show abstract

“…The presence of this simulator satisfies the need for real and heterogeneous filters of Firewalls, IP-Chains, and Access Control Lists. In the majority of the studies [44][45][46][47][48], the ClassBench tool has been used for producing the required data structure due to a need for filters and packets that are close to reality in terms of structural characteristics and statistical distribution.…”

Section: Rule Set Generation Tools and Evaluation Parametersmentioning

confidence: 99%

Ingredients to enhance the performance of two-stage TCAM-based packet classifiers in internet of things: greedy layering, bit auctioning and range encoding

Abbasi

Vakilian

Fanian

et al. 2019

J Wireless Com Network

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Using packet classification algorithms in network equipment increases packet processing speed in Internet of Things (IoT). In the hardware implementation of these algorithms, ternary content-addressable memories (TCAMs) are often preferred to other implementations. As a common approach, TCAMs are used for the parallel search to match packet header information with the rules of the classifier. In two-stage architectures of hardware-based packet classifiers, first the decision tree is created, and then the rules are distributed among its leaves. In the second step, depending on the corresponding leaves, the second part of the rules, which includes the range of source and destination ports is stored in different blocks of TCAM. Due to inappropriate storage of port range fields, the existing architectures face the problem of wasting memory and growing power consumption. This paper proposes an efficient algorithm to encode the port range. This algorithm consists of three general steps including layering, bit allocation, and encoding. A greedy algorithm in the first step places the ranges with higher weights in higher layers. Next, an auction-based algorithm allocates several bits to each layer depending on the number of the ranges in that layer. Finally, in each layer, depending on the weight order of the ranges, the bits are given values for the intended range. The evaluation results show that unlike previous methods of storing range fields, the proposed method not only increases the speed of the classification but also uses the capacity of TCAM in the second stage more efficiently.

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“…In their groundbreaking study, Varvello et al propose a more effective parallel kernel for classifying packets on GPUs on the basis of the characteristics of their memory subsystem (Varvello et al 2016). The kernel of this parallel model is designed to maximize parallelism by splitting the filter set among several blocks so that each block is responsible for checking the incoming packets only against part of the filter set.…”

Section: Related Workmentioning

confidence: 99%

“…The presence of this simulator satisfies the need for real and heterogeneous filters of Firewalls, IP-Chains, and Access Control Lists. In the majority of the studies (Deng et al 2011;Varvello et al 2016;Zheng et al 2015;Zhou et al 2014), ClassBench has been used for producing the required data structure due to a need for filters and packets that are close to reality in terms of structural characteristics and statistical distribution. In this study, this tool was used PeerJ Comput.…”

Section: Classbenchmentioning

confidence: 99%

Peer Review #1 of "Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU (v0.1)"

2019

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Packet classification is a computationally intensive, highly parallelizable task in many advanced network systems like high-speed routers and firewalls that enable different functionalities through discriminating incoming traffic. Recently, graphics processing units (GPUs) have been exploited as efficient accelerators for parallel implementation of software classifiers. The Aggregated bit vector (ABV) is a highly parallelizable packet classification algorithm. In this work, first we present a parallel kernel for running this algorithm on GPUs. Next we adapt an asymptotic analysis method which predicts any empirical result of the proposed kernel. Experimental results not only confirm the efficiency of the proposed parallel kernel but also reveal the accuracy of the analysis method in predicting important trends in experimental results.

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Multilayer Packet Classification With Graphics Processing Units

Cited by 47 publications

References 21 publications

Neural packet classification

Neural packet classification

Ingredients to enhance the performance of two-stage TCAM-based packet classifiers in internet of things: greedy layering, bit auctioning and range encoding

Peer Review #1 of "Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU (v0.1)"

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