Scalable Packet Classification Through Rulebase Partitioning Using the Maximum Entropy Hashing

Choi, Lynn; Kim, Hyogon; Kim, Sunil; Kim, Moon Hae

doi:10.1109/tnet.2009.2018618

Cited by 16 publications

(23 citation statements)

References 14 publications

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“…Note that N i=1 p(a i ) may be larger than 1. But we can perform a transformation as follows to make it fit into our assumption (4).…”

Section: Illustrationmentioning

confidence: 99%

“…As to the study of network performance via an information-theoretical approach, in [2], [3], the authors analyze network performance from the perspective of information quality and information accuracy, but the target application is not specifically on opportunistic scheduling. In [4], maximum entropy is employed to select the hash keys for partitioning the rulebase in the packet classification problem, while a technique based on minimum entropy is developed in [5] to perform carrier frequency recovery in the non-data-aided manner.…”

Section: Introductionmentioning

confidence: 99%

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Performance Bounds of Opportunistic Scheduling in Wireless Networks

Geng¹,

Lam²,

Li³

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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In this paper, we study the performance of opportunistic scheduling in wireless networks from the perspective of information and entropy. In opportunistic scheduling, we allocate a limited number of channels to a certain number of nodes so as to maximize the network performance. Due to the inherent uncertainty of the system input represented by random variables with certain probability distributions, even under the optimal scheduling strategy, we may not achieve the best network performance. In our proposed model, we mathematically formulate the relationship between system uncertainty characterized by entropy and network performance, i.e., we give the lower and upper bounds of network performance with given entropy of the uncertain input. Based on this result, we can determine quantitatively the impact of system uncertainty on the performance of of opportunistic scheduling in wireless networks.

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“…Note that N i=1 p(a i ) may be larger than 1. But we can perform a transformation as follows to make it fit into our assumption (4).…”

Section: Illustrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Bounds of Opportunistic Scheduling in Wireless Networks

Geng¹,

Lam²,

Li³

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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show abstract

“…Matching algorithm is improved in different perspectives [1][2][3][4][5][6][7][8][9][10] , a faster algorithm is proposed by MEINERS [2] , which is based on TCAM (Ternary Content Addressable Memory). In addition, the time complexity of it is generally constant.…”

Section: Introductionmentioning

confidence: 99%

“…The research of matching algorithm is conduct by the hash table, which can get well performance guarantees in the worst case. On the other hand, the average performance is very poor [4] [5] . Thus, the matching algorithm based on decision tree gets higher average performance [6] [7] , but such algorithms in the worst case performance is poor.…”

Section: Introductionmentioning

confidence: 99%

A performance-optimized firewall rules matching algorithm

Li¹,

Li²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-The algorithm of firewall rules matching designed in this paper is based on the idea of divide-and-conquer the rules set. The rules set are divided into multiple sub-sets in accordance with the protocol type. Then, accordance with the relationship between two rules, each sub-set is divided into two groups: disordered group and queue group. Furthermore, hash function is designed to match rules in disorder group, while indexing algorithm is proposed to match rules in the queue group. The analysis shows that the efficiency of the algorithm is much better than similar algorithms, greatly improving the performance of the firewall.

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“…For example, the algorithms based on hash tables have superior space performance, but their speed performance cannot be guaranteed [5] [6]. Decision-tree-based algorithms use a decision tree to divide rules into multiple linear-search groups [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Packet Classification Using Multi-iteration RFC

Tsai

Chu

Wang

2013

2013 IEEE 37th Annual Computer Software and Applications Conference Workshops

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Packet Classification is an enabling technique for the future Internet by classifying incoming packets into forwarding classes to fulfill different service requirements. It is necessary for IP routers to provide network security and differentiated services. Recursive Flow Classification (RFC) is a notable high-speed scheme for packet classification. However, it may incur high memory consumption in generating the pre-computed cross-product tables. In this paper, we propose a new scheme to reduce the memory consumption by partitioning a rule database into several subsets. The rules of each subset are stored in an independent RFC data structure to significantly alleviate overall memory consumption. We also present several refinements for these RFC data structures to significantly improve the search speed. The experimental results show that our scheme dramatically improves the storage performance of RFC.

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Scalable Packet Classification Through Rulebase Partitioning Using the Maximum Entropy Hashing

Cited by 16 publications

References 14 publications

Performance Bounds of Opportunistic Scheduling in Wireless Networks

Performance Bounds of Opportunistic Scheduling in Wireless Networks

A performance-optimized firewall rules matching algorithm

Packet Classification Using Multi-iteration RFC

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