Scalable Many-Field Packet Classification for Traffic Steering in SDN Switches

Hsieh, Cheng-Liang; Weng, Ning; Wei, Wei

doi:10.1109/tnsm.2018.2869403

Cited by 15 publications

(7 citation statements)

References 31 publications

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“…Another drawback of this hand-tuned heuristics is the absence of a global objective (e.g., tree depth or the number of nodes in the tree). Their decision making is often based on local information (the difference between the number of rules in the current node [5], the number of different ranges in different dimensions [4]). This local information is loosely related to the global objectives and that leads to their performance to be sub-optimal.…”

Section: Imentioning

confidence: 99%

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Many Field Packet Classification with Decomposition and Reinforcement Learning

Jamil,

Yang,

Weng

2022

Preprint

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Scalable packet classification is a key requirement to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine by building an efficient data structure for different ruleset with many fields. Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure. To decompose given fields of a ruleset, we consider different grouping metrics like standard deviation of individual fields and introduce a novel metric called diversity index (DI). We examine different decomposition schemes and construct decision trees for each scheme using deep reinforcement learning and compare the results. The results show that the SD decomposition metrics results in 11.5% faster than DI metrics, 25% faster than random 2 and 40% faster than random 1. Furthermore, our learning-based selection method can be applied to varying rulesets due to its ruleset independence.

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Section: Imentioning

confidence: 99%

“…One heuristic might work for one ruleset but will perform poorly for another different ruleset. As d gets bigger and bigger, even with a great heuristic, decision-tree itself couldn't keep up with increasing wirespeed requirements because of the time and space complexity associated with an increasing number of fields d [5].…”

Section: A Problem Statementmentioning

confidence: 99%

Many Field Packet Classification with Decomposition and Reinforcement Learning

Jamil,

Yang,

Weng

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another drawback of this hand-tuned heuristics is the absent of a global objective (e.g., tree depth or the number of nodes in the tree). Their decision making is often based on local information (difference between the number of rules in the current node [8], the number of different ranges in different dimensions [7] ). This local information is loosely related to the global objectives and that leads to their performance to be sub-optimal.…”

Section: Introductionmentioning

confidence: 99%

Multibit Tries Packet Classification with Deep Reinforcement Learning

Jamil,

Weng

2022

Preprint

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High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs.

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“…This has gathered the interest of many operators spanning from Internet eXchange Points (IXP) [5] to Wide Area Networks (WAN) [6], [7], [8] and data center networks [9]. All those aspects have contributed in renewing the interests of researchers and practitioners towards developing new packet classification algorithms [10], [11], [12], [13], [14] that could better cope with the stringent needs of fast lookup and matching complexity.…”

mentioning

confidence: 99%

ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era

Matoušek

Lucansky

Janeček

et al. 2022

IEEE/ACM Trans. Networking

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Packet classification, i.e., the process of categorizing packets into flows, is a first-class citizen in any networking device. Every time a new packet has to be processed, one or more header fields need to be compared against a set of pre-installed rules. This is done for basic forwarding operations, to apply security policies, application-specific processing, or quality-ofservice guarantees. A lot of research efforts have identified better lookup techniques, i.e., finding the best match between packet headers and rules, by capitalizing on the rule sets characteristics.Here, ClassBench has greatly served the community by enabling the generation of IPv4 rule sets. In this paper, we present a new tool, ClassBench-ng, that creates synthetic IPv4, IPv6, and OpenFlow rules. We start from an analysis of classification rules deployed in-the-wild and we use the findings to craft our solution. ClassBench-ng can generate a user-defined number of rules as well as an associated header trace matching them. Compared to state-of-the-art solutions, the rule set generation process is usually more accurate and it is able to produce rules matching a number of different use cases, i.e., from an IPv4 router to an OpenFlow switch, which is unique among current rule set generation tools.

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Scalable Many-Field Packet Classification for Traffic Steering in SDN Switches

Cited by 15 publications

References 31 publications

Many Field Packet Classification with Decomposition and Reinforcement Learning

Many Field Packet Classification with Decomposition and Reinforcement Learning

Multibit Tries Packet Classification with Deep Reinforcement Learning

ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era

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