FastUp: Fast TCAM Update for SDN Switches in Datacenter Networks

Wan, Ying; Song, Haoyu; Che, Hao; Xu, Yang; Wang, Yi; Zhang, Chuwen; Wang, Zhijun; Pan, Tian; Li, Hao; Jiang, Hong; Hu, Chengchen; Liu, Bin

doi:10.1109/icdcs51616.2021.00089

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…A smaller TCAM has better energy efficiency and shorter access latency [15]. Because OpenFlow switches may update the flow tables frequently, several schemes have been proposed to enable efficient TCAM updates [35][36][37].…”

Section: Many-field Packet Classification Based On Hardwarementioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

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Section: Many-field Packet Classification Based On Hardwarementioning

confidence: 99%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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“…SDN has network programmability and good scalability in both vertical and horizontal directions. Therefore, SDN is widely used in cloud data center networks [1], Internet of Things (IoT) [2], and blockchain [3].…”

Section: Introductionmentioning

confidence: 99%

TITAN: Combining a bidirectional forwarding graph and GCN to detect saturation attack targeted at SDN

Ran,

Cui,

Zhao

et al. 2024

PLoS ONE

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The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%.

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CATCAM: a 28 nm constant-time alteration TCAM enabling less than 50 ns update latency

Deng,

Xiong,

et al. 2024

Sci. China Inf. Sci.

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FastUp: Fast TCAM Update for SDN Switches in Datacenter Networks

Cited by 7 publications

References 48 publications

Packet Classification Using TCAM of Narrow Entries

Packet Classification Using TCAM of Narrow Entries

TITAN: Combining a bidirectional forwarding graph and GCN to detect saturation attack targeted at SDN

CATCAM: a 28 nm constant-time alteration TCAM enabling less than 50 ns update latency

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