Packet Classification Using GPU and One-Level Entropy-Based Hashing

Greenberg, Shlomo; Sheps, Tomer; Leon, David A.; Ben-Shimol, Yehuda

doi:10.1109/access.2020.2990331

Cited by 5 publications

(8 citation statements)

References 43 publications

(90 reference statements)

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“…Fig. 4 shows the result of mapping the rule "r: F1[1,7]F2 [2,6]→accept." to the two-dimensional matrix space M2, at this time, M2 contains a cell space, expressed as [(1,2)(7,5)].…”

Section: B Mpfpc Algorithmmentioning

confidence: 99%

“…Six cell spaces (cs1~cs6) in the two-dimensional matrix space form four coordinate projection intervals {[2,3], [4,4], [5,7], [8,9]} on the F1 dimension. The root node {a} of the sub-tree T1 corresponds to the interval [2,3], and all the associated cell spaces cs1 and cs5 form two coordinate projection intervals [2,3] and [7,8] in the F2 dimension. Two projection intervals are respectively added to the subtree T1 as the child nodes {e, f } of the root node {a}.…”

Section: Algorithm 1 Constructing Classification Decision Treementioning

confidence: 99%

“…The corresponding four interval values of each subtree root node are arranged as { [2,3], [4,4], [5,7], [8,9]} from small to large, and the corresponding branch number sequence is {l1,l2,l3,l4}={2,2,2,1}, starting from l1=2. Because |2-2.33|<|2+2-2.33|, the first subtree is taken and divided into the first computing node; Continue to consider the second number in the sequence, similarly, divide [4,4] into the second node.…”

Section: Algorithm 1 Constructing Classification Decision Treementioning

confidence: 99%

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MpFPC–A Parallelization Method for Fast Packet Classification

Cheng

Shi

2022

IEEE Access

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Packet classification is the core technology of network layer and an important means to ensure the security of network system. With the rapid development of network technology, higher requirements are put forward for the speed of network packet classification. This paper improves the traditional single thread package classification framework, A new parallelization method for fast packet classification (MpFPC) based on distributed computing is proposed, the method adopts the packet classification idea based on decision tree, but compared with the traditional algorithm, a rule mapping preprocessing process is added before constructing the classification decision tree, which effectively removes the rule redundancy and conflict, so as to avoid the rule replication problem of the traditional decision-tree-based method. In addition, the method can group the rules and data packets at the same time, which improves the packet classification efficiency. Experimental results show that MpFPC method has high classification efficiency and has obvious speed advantage compared with Uscuts method with time complexity of O(klogn). In addition, the test results also show that the classification speed of MpFPC will increase with the increasing number of computing nodes, which provides a new possible way to meet the classification wire-speed requirement.

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Section: B Mpfpc Algorithmmentioning

confidence: 99%

Section: Algorithm 1 Constructing Classification Decision Treementioning

confidence: 99%

Section: Algorithm 1 Constructing Classification Decision Treementioning

confidence: 99%

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MpFPC–A Parallelization Method for Fast Packet Classification

Cheng

Shi

2022

IEEE Access

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“…The soft errors cause bit-flip errors that makes the data in the network corrupted. Moreover, some hardware applications of soft error tolerant TCAMs using SRAM and GPU are presented in [6] and [7], respectively. This work is based on partial don't care keys used for detecting bit-flip errors.…”

Section: Figure 1 Soft Error In Tcammentioning

confidence: 99%

Optimized Multiple-Bit-Flip Soft-Errors-Tolerant TCAM using Machine Learning

Adiono

2022

JNTE

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Soft errors from radiations can change the data in electronic devices especially memory cells such as in TCAMs. The soft errors cause bit-flip errors that makes the data are corrupted in the network. This paper presents a novel machine learning for a multiple-bit-flip-tolerant TCAM that address soft errors problem using partial don't-care keys (X-keys). The general methodology is classified into two steps, i.e., statistical training and X-keys matching. First, we train the machine by collecting match probability of a filter by using X-keys that match the same locations as the search key. This method uses statistical training to determine the most efficient of number of don't cares. Moreover, in the statistical training, we also explore the maximum number of don't cares that produce best performance in covering the soft errors. Finally, the X-keys are implemented in the TCAM to correct bit-flip errors. The suitable number of don't cares in X-key is determined from the distribution of match probability of the X-keys so that the best degree of tolerance of the TCAM against soft errors is found. Match probabilities for various filters are shown. Experimental results demonstrate that the soft-error tolerance using statistical data has better soft-error tolerance than other methods. The proposed method is useful for soft-error tolerant TCAMs in routers and firewalls for robust networks.

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“…Because the reduced dependence of data and control makes them more appropriate for the parallelism on multi-core and many-core systems. Many researchers have attempted to use GPUs to solve computing intensive problems in related domains [13][14][15][16][17][18][19][20][21][22][23][24][25]. Similarly, each filter is independent while executing conflict detection, and the field content matching does not require complex computations.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts

2022

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Advanced network services, such as firewalls, policy-based routing, and virtual private networks, must rely on routers to classify packets into different flows based on packet headers and predefined filter tables. When multiple filters are overlapped, conflicts may occur, leading to ambiguity in the packet classification. Conflict detection ensures the correctness of packet classification and has received considerable attention in recent years. However, most conflict-detection algorithms are implemented on a conventional central processing unit (CPU). Compared with a CPU, a graphics processing unit (GPU) exhibits higher computing power with parallel computing, hence accelerates the execution speed of conflict detection. In this study, we employed a GPU to develop two efficient algorithms for parallel conflict detection: the general parallel conflict-detection algorithm (the GPCDA) and the enhanced parallel conflict-detection algorithm (the EPCDA). In the GPCDA, we demonstrate how to perform conflict detection through parallel execution on GPU cores. While in the EPCDA, we analyze the critical procedure in conflict detection as to reduce the number of matches required for each filter. In addition, the EPCDA adopts a workload balance method to enable load balancing of GPU execution threads, thereby significantly improving performance. The simulation results show that with the 100 K filter database, the GPCDA and the EPCDA execute conflict detection 2.8 to 13.9 and 9.4 to 33.7 times faster, respectively, than the CPU-based algorithm.

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Packet Classification Using GPU and One-Level Entropy-Based Hashing

Cited by 5 publications

References 43 publications

MpFPC–A Parallelization Method for Fast Packet Classification

MpFPC–A Parallelization Method for Fast Packet Classification

Optimized Multiple-Bit-Flip Soft-Errors-Tolerant TCAM using Machine Learning

An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts

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