Design and Implementation of a 100G Ethernet TCP/IP Protocol Stack Offload Engine

With the widespread application of technologies such as cloud computing, big data, and the Internet of Things, network bandwidth has been growing at a rate of 2 to 3 times per year. Currently, 10G networks have been widely adopted across various fields, while <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$40 \mathrm{G} / 100 \mathrm{G}$</tex> and even 400G networks are being increasingly used in data centers and other critical areas with high traffic demands. To address issues such as processor resource overload and network latency caused by TCP/IP network protocol processing in large-scale Ethernet transmissions, this paper proposes a design method for a 100G Ethernet TCP/IP offload engine [1]. The proposed engine adopts a pipelined architecture that separates the data transmission and reception channels. Additionally, a large data block fragmentation transmission mechanism is designed, supporting TCP/IP packet checksum offloading to effectively reduce the latency and processor resource consumption during large data block transmissions. Experimental results demonstrate that the 100G Ethernet TCP/IP offload engine efficiently handles network communication protocols under high-concurrency scenarios, maintains a stable network communication bandwidth, achieves a data packet transmission rate of 37 Gbps, and keeps the average latency at the microsecond level, around 200 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex>s.

Authors

Institutions

Publication Details

Published
2024-12-06
DOI
https://doi.org/10.1109/iaecst64597.2024.11117880
Primary Topic
Embedded Systems and FPGA Design
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design and Implementation of a 100G Ethernet TCP/IP Protocol Stack Offload Engine

Liguo Zhang, Zhen Wang
Embedded Systems and FPGA Design
article

Design and Implementation of a 100G Ethernet TCP/IP Protocol Stack Offload Engine

Liguo Zhang, Zhen Wang
article en

Abstract

With the widespread application of technologies such as cloud computing, big data, and the Internet of Things, network bandwidth has been growing at a rate of 2 to 3 times per year. Currently, 10G networks have been widely adopted across various fields, while <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$40 \mathrm{G} / 100 \mathrm{G}$</tex> and even 400G networks are being increasingly used in data centers and other critical areas with high traffic demands. To address issues such as processor resource overload and network latency caused by TCP/IP network protocol processing in large-scale Ethernet transmissions, this paper proposes a design method for a 100G Ethernet TCP/IP offload engine [1]. The proposed engine adopts a pipelined architecture that separates the data transmission and reception channels. Additionally, a large data block fragmentation transmission mechanism is designed, supporting TCP/IP packet checksum offloading to effectively reduce the latency and processor resource consumption during large data block transmissions. Experimental results demonstrate that the 100G Ethernet TCP/IP offload engine efficiently handles network communication protocols under high-concurrency scenarios, maintains a stable network communication bandwidth, achieves a data packet transmission rate of 37 Gbps, and keeps the average latency at the microsecond level, around 200 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex>s.

Xi’an University of Posts and Telecommunications (CN)
Affordable and clean energy
Openalex Percentile: Top 61%
Embedded Systems and FPGA Design
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.