## Context At present, we only have a single thread that reads and writes to the TUN device on all platforms. On Linux, it is possible to open the file descriptor of a TUN device multiple times by setting the `IFF_MULTI_QUEUE` option using `ioctl`. Using multi-queue, we can then spawn multiple threads that concurrently read and write to the TUN device. This is critical for achieving a better throughput. ## Solution `IFF_MULTI_QUEUE` is a Linux-only thing and therefore only applies to headless-client, GUI-client on Linux and the Gateway (it may also be possible on Android, I haven't tried). As such, we need to first change our internal abstractions a bit to move the creation of the TUN thread to the `Tun` abstraction itself. For this, we change the interface of `Tun` to the following: - `poll_recv_many`: An API, inspired by tokio's `mpsc::Receiver` where multiple items in a channel can be batch-received. - `poll_send_ready`: Mimics the API of `Sink` to check whether more items can be written. - `send`: Mimics the API of `Sink` to actually send an item. With these APIs in place, we can implement various (performance) improvements for the different platforms. - On Linux, this allows us to spawn multiple threads to read and write from the TUN device and send all packets into the same channel. The `Io` component of `connlib` then uses `poll_recv_many` to read batches of up to 100 packets at once. This ties in well with #7210 because we can then use GSO to send the encrypted packets in single syscalls to the OS. - On Windows, we already have a dedicated recv thread because `WinTun`'s most-convenient API uses blocking IO. As such, we can now also tie into that by batch-receiving from this channel. - In addition to using multiple threads, this API now also uses correct readiness checks on Linux, Darwin and Android to uphold backpressure in case we cannot write to the TUN device. ## Configuration Local testing has shown that 2 threads give the best performance for a local `iperf3` run. I suspect this is because there is only so much traffic that a single application (i.e. `iperf3`) can generate. With more than 2 threads, the throughput actually drops drastically because `connlib`'s main thread is too busy with lock-contention and triggering `Waker`s for the TUN threads (which mostly idle around if there are 4+ of them). I've made it configurable on the Gateway though so we can experiment with this during concurrent speedtests etc. In addition, switching `connlib` to a single-threaded tokio runtime further increased the throughput. I suspect due to less task / context switching. ## Results Local testing with `iperf3` shows some very promising results. We now achieve a throughput of 2+ Gbit/s. ``` Connecting to host 172.20.0.110, port 5201 Reverse mode, remote host 172.20.0.110 is sending [ 5] local 100.80.159.34 port 57040 connected to 172.20.0.110 port 5201 [ ID] Interval Transfer Bitrate [ 5] 0.00-1.00 sec 274 MBytes 2.30 Gbits/sec [ 5] 1.00-2.00 sec 279 MBytes 2.34 Gbits/sec [ 5] 2.00-3.00 sec 216 MBytes 1.82 Gbits/sec [ 5] 3.00-4.00 sec 224 MBytes 1.88 Gbits/sec [ 5] 4.00-5.00 sec 234 MBytes 1.96 Gbits/sec [ 5] 5.00-6.00 sec 238 MBytes 2.00 Gbits/sec [ 5] 6.00-7.00 sec 229 MBytes 1.92 Gbits/sec [ 5] 7.00-8.00 sec 222 MBytes 1.86 Gbits/sec [ 5] 8.00-9.00 sec 223 MBytes 1.87 Gbits/sec [ 5] 9.00-10.00 sec 217 MBytes 1.82 Gbits/sec - - - - - - - - - - - - - - - - - - - - - - - - - [ ID] Interval Transfer Bitrate Retr [ 5] 0.00-10.00 sec 2.30 GBytes 1.98 Gbits/sec 22247 sender [ 5] 0.00-10.00 sec 2.30 GBytes 1.98 Gbits/sec receiver iperf Done. ``` This is a pretty solid improvement over what is in `main`: ``` Connecting to host 172.20.0.110, port 5201 [ 5] local 100.65.159.3 port 56970 connected to 172.20.0.110 port 5201 [ ID] Interval Transfer Bitrate Retr Cwnd [ 5] 0.00-1.00 sec 90.4 MBytes 758 Mbits/sec 1800 106 KBytes [ 5] 1.00-2.00 sec 93.4 MBytes 783 Mbits/sec 1550 51.6 KBytes [ 5] 2.00-3.00 sec 92.6 MBytes 777 Mbits/sec 1350 76.8 KBytes [ 5] 3.00-4.00 sec 92.9 MBytes 779 Mbits/sec 1800 56.4 KBytes [ 5] 4.00-5.00 sec 93.4 MBytes 783 Mbits/sec 1650 69.6 KBytes [ 5] 5.00-6.00 sec 90.6 MBytes 760 Mbits/sec 1500 73.2 KBytes [ 5] 6.00-7.00 sec 87.6 MBytes 735 Mbits/sec 1400 76.8 KBytes [ 5] 7.00-8.00 sec 92.6 MBytes 777 Mbits/sec 1600 82.7 KBytes [ 5] 8.00-9.00 sec 91.1 MBytes 764 Mbits/sec 1500 70.8 KBytes [ 5] 9.00-10.00 sec 92.0 MBytes 771 Mbits/sec 1550 85.1 KBytes - - - - - - - - - - - - - - - - - - - - - - - - - [ ID] Interval Transfer Bitrate Retr [ 5] 0.00-10.00 sec 917 MBytes 769 Mbits/sec 15700 sender [ 5] 0.00-10.00 sec 916 MBytes 768 Mbits/sec receiver iperf Done. ```
This is a Next.js project bootstrapped with
create-next-app.
Getting Started
First, install dependencies and populate the timestamps.json file:
pnpm setup
Next, create files .env.local and .env.development.local in this directory.
Put this in .env.local:
NEXT_PUBLIC_MIXPANEL_TOKEN=""
NEXT_PUBLIC_GOOGLE_ANALYTICS_ID=""
NEXT_PUBLIC_LINKEDIN_PARTNER_ID=""
FIREZONE_DEPLOYED_SHA=""
And this in .env.development.local:
# Created by Vercel CLI
EDGE_CONFIG=""
FIREZONE_DEPLOYED_SHA=""
SITE_URL=""
VERCEL_DEEP_CLONE=""
After that, make sure to contact the team for their values.
Then, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
Open http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page
auto-updates as you edit the file.
Linting
This project uses Prettier to format code and ensure a consistent style. Use the .prettierrc.json in the root of this repo to configure your editor.
Learn More
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
Deploy on Vercel
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.