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

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}, 
}