Encapsulate spdlog in UpfBpfConfig

📝 NOTE 1: This is still an experimental project. The releases were just for quick access of the artifacts presented in the publications (See docs folder.)

📝 NOTE 2: There is task force to improve and integrate the upf-bpf project inside the OAI 5G UPF. See here for more information.

5G UPF using BPF/XDP

An open source C++ library powered by BPF/XDP for user planes in the mobile core network (5G/LTE).

The key pillars of this project are:

In-kernel fast packet processing
Flexible and programmable dataplane
Portable to different systems

These points are achieved mainly by BPF/XDP and CO-RE (Compile Once - Run Everywhere) technologies.

This project is based on the following 3GPP Technical Specification:

LTE; 5G; Interface between the Control Plane and the User Plane nodes (3GPP TS 29.244 version 16.5.0 Release 16)
5G; System architecture for the 5G System (5GS) (3GPP TS 23.501 version 16.5.0 Release 16)

The main goal is to enable in-kernel fast packet processing in third-party UPF/5G or SPGWu/LTE components in order to:

Boost them for those which does not have any fast packet processing enabled, or
Co-locate them with other fast packet processing solutions (e.g. DPDK)

Possible scenarios that take advantage of this type of technology: MEC, 5G NPN (Non Public Networks), on-premise, 5G enterprise, and much more.

Design

The library is divided in layers:

Management Layer: An user space layer responsible to receive requests from the third-party UPF/SPGWu components to manage PFCP sessions and BPF programs lifecycle
Datapath Layer: A kernel space layer representing by BPF/XDP programs responsible to handle the user traffic (datapath) for fast packet processing

The high level design is shown in figure below.

The library has a component, called PFCP Session Manager, which is a C++ API responsible for managing PFCP (Packet Forwarding Control Protocol) sessions. This layer selects the highest PDR and its rules for each PFCP session to compose the datapath Linux kernel. It is the eBPF Program Manager, which is responsible for loading/unloading the BPF programs. The BPF program is mapped to each rule defined in highest precedence PDR (e.g. FAR) for each PFCP session created. The fast path is composed of three main functions: Parser, Detection (both in entry BPF section) and Rule. The image below shows this in more detail.

Parser: responsible to parse the GTP and UDP packets
Detector: responsible for matching the Packet Detection Information (TEID, source interface and UE IP address) with the header of the packet. If matches, so the rules of the PFCP session context must be applied
Rule: responsible to encapsulate the logic of one rule (e.g. FAR, QER, BAR, etc). Depending on the PFCP session context, it might have more than one rule available in the datapath.

The FAR is mandatory according to the 3GPP specification

A low-level design (Datapath Layer) is shown below.

Activity Diagrams

Figure: PFCP session creation activity diagram in Management Layer.

Figure: On new packet received activity diagram in Datapath Layer. There are two pipeline: one with only the FAR and the other one with QER and FAR.

Features

As described in 3GPP TS 29.244, the Information Elements (IEs) are part of the PFCP context. The PFCP context is created by sending a PFCP Session Establishment Request message. The main features supported in this project are:

Management Layer - CRUD

PFCP Session
PDR (Packet Detection Rule)
FAR (Forwarding Action Rule)

Fast Datapath Layer

UDP and GTP parse
Traffic detection based on PDR
Traffic forwarding based on FAR

The logical data model between PFCP Session and IEs is shown in the image below. For more detail, see 3GPP TS 29.244 version 16.5.0 Release 16.

🚧 Future Work

QER (QoS Enforcement Rule)
CO-RE for tracing.
PoC with OpenAirInterface

Main Dependencies

Core

libbpf
bpftool
spdlog
clang >= version 3.4.0
llvm >= version 3.7.1
kernel-headers => version 5.4
cmake >= 3.16

Test

trex v2.86
sysstat (mpstat)
ethtool
gtest

First Steps

First of all, make sure you have installed git-lfs. The LFS repository is used to store the bpftool binary.

After downloaded and installed it, clone this repository:

git clone https://github.com/navarrothiago/upf-bpf.git

After cloning the repository, configure your env.sh file (on the repository root folder) to match your dev or test environment, using the .env.sample.sh file as a template

The project uses a docker container to build the UPF library. The command below will provision the docker image with all the project dependencies.

📝 You'll need the Docker Container Runtime package and the Docker Compose utility to set up the dev or test environment

make docker-build

After that, run the container with:

make docker-run

You can also use the vscode development container feature to build the image and login into the container. Check here to understand how to open the devcontainer.json file.

Inside the container, compile the dependencies with

make setup

Build and Installation

The library is built and installed with

make install

The package folder is created with the headers, library and some binaries for testing.

package
├── bin       # Contains binaries for testing
├── include   # Contains headers
├── lib       # Contains libupf_xdp.a library
└── tests     # Contains scripts for testing

How to Test

The test is based on RFC2544-like measurements. The testbed is composed of two servers: Trex Traffic Generator and HTTP API + upf-bpf.

Requirements:

Both machines with Ubuntu 20.04.02 LTS installed with Linux kernel v5.4.0-72-generic. One machine is used to generate user traffic with TRex Traffic Generator and the other is the DUT (Device Under Test) where the upf-bpf is deployed.
Both machine contains two NICs
- For the Trex traffic generator, both NICs drivers must support DPDK. Check out the Table 5 - Supported NICs
- For DUT, both NICs drivers must support XDP. Check out here.

Test environment:

Network topology:

Setup Trex Traffic Generator Server

This machine must have installed the Trex traffic generator. You can check the trex manual or you can based on the scripts that are called when make trex is executed (unstable).

Setup HTTP API + upf-bpf Server

After running make install inside the docker container, copy the application ./package/bin/api to the DUT machine. Your host must have kernel >= v5.4.

Steps:

Run Trex Traffic Generator
Run HTTP API + upf-bpf
Configure interfaces (/configure)
Create PFCP Session context (/createSession)
Configure the number of Rx queue in DUT
Generate the GTP/UDP flows (pkt size = 64B)
Collects metrics (CPU load, packet loss, throughput)

For step 1, see the trex manual

For step 2, run HTTP API + upf-bpf with:

sudo ./bin/api 10.1.1.27 80

For steps 3 and 4, there are Postman files are available: Uplink and Downlink. Check the JSON message for each step.

For steps 5, 6 and 7, it was implemented a Python script to automate the process. The script executes the test case varying the number of the rx queue. In the end, a report is generated based on JSON format with all the metrics (i.e throughput and CPU load) for each execution. The flows leverage the Trex Field Engine to generate the flows. You can also generate the flow manually in the Trex Traffic Generator server.

In order to execute the script, run the following command inside the docker container:

export PYTHONPATH='/workspaces/tests/trex/trex_client/interactive/'

# example to generate GTP flow with 12mpps of throughput.
./tests/trex/test_cases/run.py -m 12mpps -p <password_trex_server> -f gtp
# example to generate UDP flow with 100% of throughput.
./tests/trex/test_cases/run.py -m 100% -p <password_trex_server> -f udp

📝 The env.sh file must be configured properly in order to have a successful execution.

There is a tmux session script available here that were developed to a specific scenario. Some parameters are hardcoded. Feel free to change according to your needs. If you need any help, open an issue or contact me. PR are welcome!!

⚠️ Some scripts were developed to work in one environment. As you can see in .env.sample.sh, there are variables to configure the jump server, trex version, GTP and UDP interfaces (downlink and uplink), etc. You might face some problems when trying to execute some of them, because they were not exhaustive tests in other environments.

UTs

Some UTs were developed for the Session Management layer. You can execute inside the container:

make config-veth-pair
make build-tests
make run-session-manager-tests

If you face any problem, feel free to open an issue or contact me.

🚀 Benchmark

Setup: Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz, 32GiB of the DRAM, 15M of L3 cache, 6 cores (hyper-threading disabled), dual-port 82599ES 10-Gigabit SFI/SFP+ NIC. Both machines have Ubuntu 20.04.02 LTS installed with Linux kernel v5.4.0-72-generic.

Disable the hyper-threading with echo off > /sys/devices/system/cpu/smt/control

Downlink	Uplink

Check the Jupyter notebook to see how the graphics are generated.

📝 For more graphics, check this folder.

Time spent to inject BPF program into the kernel after receive the PFCP Establishement Request message.

Version	BPF section	BPF Insn	Injection (ms)
v1.0.0	PFCP Session	402	27
v2.0.0	FAR	272	1

📝 The main reason is due the logic related to lookup the PDR is implemented in the control plane (Management Layer) in v2.0.0 and in the data plane in v1.0.0. The PFCP session was composed with only one FAR.

Jupyter Notebook

The data collected for uplink (GTP decapsulation) and downlink (GTP encapsulation) e2e testing are available:

Firstly, you need to install the python dependencies:

# create our virtual environment
python3 -m venv env
# activate our virtual environment
source env/bin/activate
# install dependencies
pip3 install -r requirements.txt

Now, you can execute notebook using Jupyter.

Tree

├── build: Generated build directory.
├── cmake: Cmake files configuration directory
├── extern: Submodule repositories
├── include: Include files
├── samples: Samples like XDP BPF hello world
├── src: Source files directory
├── tests: UTs, HTTP API srcs, scripts for testing, trex installation
├── Makefile: Encapsulate cmake calls for build, run samples, clean, etc
└── README.md: Readme file

Trex Traffic Generator

If you faced FileNotFoundError: [Errno 2] No such file or directory: b'liblibc.a' when executing trex, for instance:

 cd tests/scripts/../trex/v2.87; sudo ./trex-console --port 1235 --async_port 1236

Create a symbolic from libc.a -> liblibc.a (Manjaro)

sudo ln -s -f /usr/lib64/libc.a /usr/lib64/liblibc.a

Contribute

If you think this could be better, please open an issue or start a discussion.

PRs ARE WELCOME 👍

⭐ BibTeX

The paper is available here.

@INPROCEEDINGS{Amar2110:Kernel,
AUTHOR="Thiago Arruda Navarro do Amaral and Raphael {Vicente Rosa} and David Moura
and Christian {Esteve Rothenberg}",
TITLE="An {In-Kernel} Solution Based on {XDP} for {5G} {UPF:} Design, Prototype
and Performance Evaluation",
BOOKTITLE="2021 1st Joint International Workshop on Network Programmability and
Automation (NetPA 2021)",
ADDRESS="Izmir, Turkey, Turkey",
DAYS=24,
MONTH=oct,
YEAR=2021,
KEYWORDS="5G; XDP; UPF",
ABSTRACT="The edge computing infrastructure can scale from datacenters to single
device. The well-known technology for fast packet processing is DPDK, which
has outstanding performance regarding the throughput and latency. However,
there are some drawbacks when the usage is done in the edge: (i) the
polling mechanism for packet processing keeps the CPU exclusively occupied
even if there is no traffic, leading to wasted resources; and (ii) DPDK
interface becomes unavailable for the applications inside the host, so the
integration between a non-DPDK application and a DPDK application becomes a
hard task. In this paper, we propose an open-source in-kernel 5G UPF
solution based on 3GPP Release 16 to be deployed in a restrictive
environment like MEC, where MEC host and UPF are collocated with the Base
Station, sharing the same computational and network resources. The solution
leverages the eBPF/XDP, a novel Linux kernel technology for fast packet
processing. We show it can scale and achieve 10 Mpps using only 60\% of the
CPU with 6 cores."
}

📓 Contacts

Discord Server
Mail: <navarro (dot) ime (at) gmail [dot] com>
Twitter: @navarr0thiag0

	def create_gtp_pkt_flow(size, ip_min, ip_max, nflows, field):
	print("{} flow will be generated...".format(nflows))
	base_pkt = Ether()/IP(src="172.20.16.99", dst="192.168.15.12")/UDP(dport=2152) / \
	GTP_U_Header(teid=100) / \
	IP(src="10.10.10.10", dst="10.1.3.27", version=4)/UDP(dport=1234)
	pad = max(0, size - len(base_pkt)) * 'x'
	return STLPktBuilder(pkt=base_pkt/pad, vm=create_vm(ip_min, ip_max, nflows, field))

navarrothiago / upf-bpf Goto Github PK

upf-bpf's Introduction

5G UPF using BPF/XDP

Design

Activity Diagrams

Features

🚧 Future Work

Main Dependencies

First Steps

Build and Installation

How to Test

Setup Trex Traffic Generator Server

Setup HTTP API + upf-bpf Server

UTs

🚀 Benchmark

Jupyter Notebook

Tree

Trex Traffic Generator

Contribute

⭐ BibTeX

📓 Contacts

ℹ️ References

upf-bpf's People

Contributors

Stargazers

Watchers

Forkers

upf-bpf's Issues

Test Case:

Test Case:

References:

Recommend Projects

Recommend Topics

Recommend Org