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LiteFlow Logo

A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath

Build Status

Please refer to our SIGCOMM '22 paper for details.

ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.

Tested Kernel Version: 4.15.0-173-generic

Getting Started

Installation

  1. Clone the repo

    git clone https://github.com/snowzjx/liteflow.git

  2. Change the directory to LiteFlow

    cd liteflow

  3. Install required packages

    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt

  4. Compile

    make all

  5. Install LiteFlow kernel module

    make module_install

    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.

  6. Install TCP Congestion Control module

    make tcp_kernel_install

    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.

    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel

Examples

Snapshot Generation

cd script
./snapshot_generation.sh

This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.

Snapshot Update

cd script
./active_standby_switch.sh

You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.

[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...

Batched Training Data Delivery

cd script
./fetch_data_from_kernel.sh

The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.

LiteFlow Userspace Demo

cd bin
python3 lf.py

You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...

Citation

@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }

About

A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath

Topics

datapathkernelkernel-modulenetworkingneural-networks

Resources

Readme
GPL-2.0 license
Activity

Stars

28 stars

Watchers

1 watching

Forks

6 forks
Report repository

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GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
Skip to content
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LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
Skip to content
BranchesTags
Repository files navigation
LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
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Used by
Contributors
Languages

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
Skip to content
BranchesTags
Repository files navigation
LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
Skip to content
BranchesTags
Repository files navigation
LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
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LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
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datapathkernelkernel-modulenetworkingneural-networks
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Readme
GPL-2.0 license
Activity
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28 stars
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1 watching
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6 forks
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GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
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Repository files navigation
LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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})();
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GitHub - snowzjx/liteflow: A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath · GitHub
Skip to content
BranchesTags
Repository files navigation
LiteFlow Logo
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Build Status
Please refer to our SIGCOMM '22 paper for details.
ATTENTION: This project is for research purposes and please do not use it in a production environment. Some of the code is not opensourced due to compatibility issues now and we are working on it to release them soon. However, you can still experience some of the key components mentioned in our paper.
Tested Kernel Version: 4.15.0-173-generic
Getting Started
Installation
  1. Clone the repo
    git clone https://github.com/snowzjx/liteflow.git
  2. Change the directory to LiteFlow
    cd liteflow
  3. Install required packages
    apt install -y build-essential libnl-3-dev libnl-genl-3-dev pkg-config linux-headers-generic python3 python3-pip 
    pip3 install -U pip
pip3 install -r bin/requirements.txt
  4. Compile
    make all
  5. Install LiteFlow kernel module
    make module_install
    You can check if the LiteFlow kernel module is successfully installed by executing lsmod to see if lf_kernel is there.
  6. Install TCP Congestion Control module
    make tcp_kernel_install
    You can check if the LiteFlow TCP Congestion Control is successfully installed by executing lsmod to see if lf_tcp_kernel is there.
    You can also check if the TCP Congestion Control is successfully activated by executing sysctl net.ipv4.tcp_congestion_control to see whether is value is lf_tcp_kernel
Examples
Snapshot Generation
cd script
./snapshot_generation.sh
This command tries to generate 3 Aurora snapshots from the model in liteflow/data/. You can verify if the command is successful by checking if lf_model_1.ko, lf_model_2.ko, and lf_model_3.ko are generated. You can also check the generated source code by looking into lf_model_1.c, lf_model_2.c, and lf_model_3.ko files. Note here, we generate 3 identical snapshots just for demo purposes.
Snapshot Update
cd script
./active_standby_switch.sh
You expect a dmesg output showing that LiteFlow uses different neural networks to serve different flows.
[ 2480.330440] Current slot 0 is registered with model: -1
[ 2480.330440] Current slot 1 is registered with model: 1
[ 2480.330440] Current active slot is: 1
...
[ 2482.826293] Using model with uuid: 1 for inference...
...
[ 2483.578519] Current slot 0 is registered with model: 2
[ 2483.578519] Current slot 1 is registered with model: 1
[ 2483.578519] Current active slot is: 0
...
[ 2483.823231] Using model with uuid: 2 for inference...
...
[ 2484.844727] Current slot 0 is registered with model: 2
[ 2484.844727] Current slot 1 is registered with model: 3
[ 2484.844727] Current active slot is: 1
...
[ 2485.099799] Using model with uuid: 3 for inference...
...
Batched Training Data Delivery
cd script
./fetch_data_from_kernel.sh
The screen should print all training data. Note: different kernel settings may cause different outputs, please change the code accordingly.
LiteFlow Userspace Demo
cd bin
python3 lf.py
You expect to see a demo output of how LiteFlow generates a snapshot, evaluates a new neural network with the existing snapshot and updates the snapshot. Note: for compatibility issues, there are lots of places left blank in the userspace program of LiteFlow, users can fill in the blanks with their own needs...
Citation
@inproceedings{liteflow, author = {Junxue Zhang and Chaoliang Zeng and Hong Zhang and Shuihai Hu and Kai Chen}, title = {LiteFlow: Towards High-Performance Adaptive Neural Networks for Kernel Datapath}, year = {2022}, booktitle = {Proceedings of the ACM SIGCOMM 2022 Conference}, }
About
A Hybrid Framework to Build High-performance Adaptive Neural Networks for Kernel Datapath
Topics
datapathkernelkernel-modulenetworkingneural-networks
Resources
Readme
GPL-2.0 license
Activity
Stars
28 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages