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Proceedings ArticleDOI

Optimizing Packet Classification on FPGA

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TLDR
In this paper , the authors proposed optimizations to the DCFL algorithm and overall packet processing hardware architecture to maximize the throughput and minimize the resource strain, which can achieve up to a 76 % increase in the throughput of the packet classification.
Abstract
Packet classification is a crucial time-critical operation for many different networking tasks ranging from switching or routing to monitoring and security devices like firewalls or IDS. Accelerated architectures implementing packet classification must satisfy the ever-growing demand for current high-speed networks. However, packet classification is generally used together with other packet processing algorithms, which decreases the available hardware resources on the FPGA chip. The introduction of the P4 language requires the packet classification to be even more flexible while maintaining a high throughput with limited resources. Thus, we need flexible and high-performance architectures to balance processing speed and hardware resources for specific types of rules. DCFL algorithm provides high performance and flexibility. Therefore, we propose optimizations to the DCFL algorithm and overall packet processing hardware architecture. The goal is to maximize the throughput and minimize the resource strain. The main idea of the approach is to analyze the ruleset, identify some conflicting rules and offload these rules to other hardware modules. This approach allows us to process packets faster, even in the worst-case scenarios. Moreover, we can fit more packet processing into the FPGA and fine-tune the packet processing architecture to meet a specific network application’s throughput and resource demands. With the proposed optimizations we can achieve up to a 76 % increase in the throughput of the packet classification. Alternatively, we can achieve up to a 37 % decrease in resources needed.

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References
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Journal ArticleDOI

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TL;DR: A novel packet classification architecture called BV-TCAM is presented, which is implemented for an FPGA-based Network Intrusion Detection System (NIDS), which can report multiple matches at gigabit per second network link rates.
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ClassBench: a packet classification benchmark

TL;DR: This work presents ClassBench, a suite of tools for benchmarking packet classification algorithms and devices, and seeks to eliminate the significant access barriers to realistic test vectors for researchers and initiate a broader discussion to guide the refinement of the tools and codification of a formal benchmarking methodology.
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