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High-rate Delay Tolerant Network

Overview

Delay Tolerant Networking (DTN) has been identified as a key technology to facilitate the development and growth of future space networks. DTN is an overlay network that uses the bundle protocol to connect once disparate one-to-one links. Bundles are the primary unit of data in a DTN, and can be of essentially any size. Existing DTN implementations have operated in constrained environments with limited resources resulting in low data speeds, and cannot utilize more than a fraction of available system capacity. However, as various technologies have advanced, data transfer rates and efficiency have also advanced. To date, most known implementations of DTN have been designed to operate on spacecraft.

High-rate Delay Tolerant Networking (HDTN) takes advantage of modern hardware platforms to substantially reduce latency and improve throughput compared to today’s DTN operations. HDTN maintains compatibility with existing deployments of DTN that conform to IETF RFC 5050. At the same time, HDTN defines a new data format better suited to higher-rate operation. It defines and adopts a massively parallel pipelined and message-oriented architecture, allowing the system to scale gracefully as its resources increase. HDTN’s architecture also supports hooks to replace various processing pipeline elements with specialized hardware accelerators. This offers improved Size, Weight, and Power (SWaP) characteristics while reducing development complexity and cost.

The HDTN team has completed an extensive review of the major DTN protocols and specifications. Our requirements analysis is captured in E-20225TM_Cover.indd (nasa.gov). Also see the HDTN Wiki and the HDTN User Guide for more information.

Architecture

HDTN is written in C++, and is designed to be modular. These modules include:

  • Ingress - Processes incoming bundles.
  • Storage - Stores bundles to disk.
  • Router - Calculates the next hop for the bundle and sets links up/down based on contact plan.
  • Egress - Forwards bundles to the proper outduct and next hop.
  • Telemetry Command Interface - Web interface that displays the operations and data for HDTN.

Build Environment

Tested Platforms

  • Linux
    • Ubuntu 20.04.2 LTS
    • Debian 10
    • RHEL (Red Hat Enterprise Linux) 8
    • ARM64
  • Windows
    • Windows 10 (64-bit)
    • Windows Server 2022 (64-bit)
    • Windows Server 2019 (64-bit)
  • macOS
    • Apple Silicon M2 on Ventura
    • Intel x64 on Ventura
  • FreeBSD Intel x64
  • OpenBSD Intel x64
  • Raspbian
  • ARM on x86

Dependencies

HDTN build environment requires:

  • CMake version 3.12 minimum
  • Boost library version 1.66.0 minimum, version 1.69.0 for TCPCLv4 TLS version 1.3 support
  • ZeroMQ (tested with version 4.3.4)
  • OpenSSL (recommended version 1.1.1). If OpenSSL is not available, disable OpenSSL support via the CMake cache variable ENABLE_OPENSSL_SUPPORT:BOOL
  • On Linux: gcc version 9.3.0 (Debian 8.3.0-6)
  • On Windows: see the Windows Build Instructions
  • Target: x86_64-linux-gnu
  • Tested platforms: Ubuntu 20.04.2 LTS, Debian 10, Windows 10 (Visual Studio), Windows Server 2019 (Visual Studio), Windows Server 2022 (Visual Studio), and Mac

These can be installed on Linux with:

  • On Ubuntu
sudo apt-get install cmake build-essential libzmq3-dev libboost-dev libboost-all-dev openssl libssl-dev
  • On RHel
sudo dnf install epel-release
sudo dnf install cmake boost-devel zeromq zeromq-devel gcc-c++ libstdc++-devel

macOS Dependencies

  • ZeroMQ

On macOS, ZeroMQ needs to be built from source and installed in the /usr/local directory which can be done with:

sudo git clone https://github.com/zeromq/libzmq /usr/local/libzmq
cd /usr/local/libzmq
git checkout v4.3.4
sudo mkdir build
sudo chmod -R 777 .
cd build
cmake ..
make -j4
make install
  • OpenSSL and gnutls

First, install Homebrew, which is a package manager for macOS:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Finally, install OpenSSL and gnutls using Homebrew:

brew install openssl gnutls

Known issue

  • Ubuntu distributions may install an older CMake version that is not compatible
  • Some processors may not support hardware acceleration or the RDSEED instruction, both ON by default in the cmake file

Notes on C++ Standard Version

HDTN build environment sets by default the CMake cache variable HDTN_TRY_USE_CPP17 to ON.

  • If set to ON, CMake will test if the compiler supports C++17. If the test fails, CMake will fall back to C++11 and compile HDTN with C++11. If the test passes, CMake will compile HDTN with C++17.
  • If set to OFF, CMake will compile HDTN with C++11.
  • With Visual Studio 2017 version 15.7 and later versions, if C++17 is enabled, CMake will set the compile option /Zc:__cplusplus to make the __cplusplus preprocessor macro accurate for Visual Studio. (Otherwise, without this compile option, by default, Visual Studio always returns the value 199711L for the __cplusplus preprocessor macro.)
  • Throughout the HDTN codebase, C++17 optimizations exist, usually within a #if(__cplusplus >= 201703L) block.

Optional X86 Hardware Acceleration

HDTN build environment sets by default two CMake cache variables to ON: USE_X86_HARDWARE_ACCELERATION and LTP_RNG_USE_RDSEED.

  • If you are building natively (i.e. not cross-compiling) then the HDTN CMake build environment will check the processor's CPU instruction set as well as the compiler to determine which HDTN hardware accelerated functions will both build and run on the native host. CMake will automatically set various compiler defines to enable any supported HDTN hardware accelerated features.
  • If you are cross-compiling then the HDTN CMake build environment will check the compiler only to determine if the HDTN hardware accelerated functions will build. It is up to the user to determine if the target processor will support/run those instructons. CMake will automatically set various compiler defines to enable any supported HDTN hardware accelerated features if and only if they compile.
  • Hardware accelerated functions can be turned off by setting USE_X86_HARDWARE_ACCELERATION and/or LTP_RNG_USE_RDSEED to OFF in the CMakeCache.txt.
  • If you are building for ARM or any non X86-64 platform, USE_X86_HARDWARE_ACCELERATION and LTP_RNG_USE_RDSEED must be set to OFF.

If USE_X86_HARDWARE_ACCELERATION is turned on, then some or all of the following various features will be enabled if CMake finds support for these CPU instructions:

  • Fast SDNV encode/decode (BPv6, TCPCLv3, and LTP) requires SSE, SSE2, SSE3, SSSE3, SSE4.1, POPCNT, BMI1, and BMI2.
  • Fast batch 32-byte SDNV decode (not yet implemented into HDTN but available in the common/util/Sdnv library) requires AVX, AVX2, and the above "Fast SDNV" support.
  • Fast CBOR encode/decode (BPv7) requires SSE and SSE2.
  • Some optimized loads and stores for TCPCLv4 requires SSE and SSE2.
  • Fast CRC32C (BPv7 and a storage hash function) requires SSE4.2.
  • The HDTN storage controller will use BITTEST if available. If BITTEST is not available, it will use ANDN if BMI1 is available.

If LTP_RNG_USE_RDSEED is turned on, this feature will be enabled if CMake finds support for this CPU instruction:

  • An additional source of randomness for LTP's random number generator requires RDSEED. You may choose to disable this feature for potentially faster LTP performance even if the CPU supports it.

Storage Capacity Compilation Parameters

HDTN build environment sets by default two CMake cache variables: STORAGE_SEGMENT_ID_SIZE_BITS and STORAGE_SEGMENT_SIZE_MULTIPLE_OF_4KB.

  • The flag STORAGE_SEGMENT_ID_SIZE_BITS must be set to 32 (default and recommended) or 64. It determines the size/type of the storage module's segment_id_t Using 32-bit for this type will significantly decrease memory usage (by about half).
    • If this value is 32, The formula for the max segments (S) is given by S = min(UINT32_MAX, 64^6) = ~4.3 Billion segments since segment_id_t is a uint32_t. A segment allocator using 4.3 Billion segments uses about 533 MByte RAM), and multiplying by the minimum 4KB block size gives ~17TB bundle storage capacity. Make sure to appropriately set the totalStorageCapacityBytes variable in the HDTN json config so that only the required amount of memory is used for the segment allocator.
    • If this value is 64, The formula for the max segments (S) is given by S = min(UINT64_MAX, 64^6) = ~68.7 Billion segments since segment_id_t is a uint64_t. Using a segment allocator with 68.7 Billion segments, when multiplying by the minimum 4KB block size gives ~281TB bundle storage capacity.
  • The flag STORAGE_SEGMENT_SIZE_MULTIPLE_OF_4KB must be set to an integer of at least 1 (default is 1 and recommended). It determines the minimum increment of bundle storage based on the standard block size of 4096 bytes. For example:
    • If STORAGE_SEGMENT_SIZE_MULTIPLE_OF_4KB = 1, then a 4KB * 1 = 4KB block size is used. A bundle size of 1KB would require 4KB of storage. A bundle size of 6KB would require 8KB of storage.
    • If STORAGE_SEGMENT_SIZE_MULTIPLE_OF_4KB = 2, then a 4KB * 2 = 8KB block size is used. A bundle size of 1KB would require 8KB of storage. A bundle size of 6KB would require 8KB of storage. A bundle size of 9KB would require 16KB of storage. If STORAGE_SEGMENT_ID_SIZE_BITS=32, then bundle storage capacity could potentially be doubled from ~17TB to ~34TB.

For more information on how the storage works, see module/storage/doc/storage.pptx in this repository.

Logging Compilation Parameters

Logging is controlled by CMake cache variables:

  • LOG_LEVEL_TYPE controls which messages are logged. The options, from most verbose to least verbose, are TRACE, DEBUG, INFO, WARNING, ERROR, FATAL, and NONE. All log statements using a level more verbose than the provided level will be compiled out of the application. The default value is INFO.
  • LOG_TO_CONSOLE controls whether log messages are sent to the console. The default value is ON.
  • LOG_TO_ERROR_FILE controls whether all error messages are written to a single error.log file. The default value is OFF.
  • LOG_TO_PROCESS_FILE controls whether each process writes to their own log file. The default value is OFF.
  • LOG_TO_SUBPROCESS_FILE controls whether each subprocess writes to their own log file. The default value is OFF.

Getting Started

Build HDTN

If building on Windows, see the Windows Build Instructions. To build HDTN in Release mode (which is now the default if -DCMAKE_BUILD_TYPE is not specified), follow these steps:

  • export HDTN_SOURCE_ROOT=/home/username/HDTN
  • cd $HDTN_SOURCE_ROOT
  • mkdir build
  • cd build
  • cmake ..
  • make -j8
  • make install

Note: By Default, BUILD_SHARED_LIBS is OFF and hdtn is built as static To use shared libs, edit CMakeCache.txt, set BUILD_SHARED_LIBS:BOOL to ON and add fPIC to the Cmakecache variable: CMAKE_CXX_FLAGS_RELEASE:STRING=-03 -DNDEBUG -fPIC

Note: If building HDTN from a cloned git repo (with a .git directory in the source root), HDTN's CMake will get the Git SHA-1 hash so that not only will HDTN's logger print the HDTN version, but also it will print HDTN's latest commit hash. However, if building HDTN from zip, tar, tar.bz2, or tar.gz, HDTN's CMake can detect the commit hash within the archive file's comment header by running cmake with the following argument that points to the archive file from where HDTN was extracted from:

  • cmake -DHDTN_ARCHIVE_FILE=/path/to/hdtn.zip ..

ARM Platforms

HDTN has been tested on various ARM platforms such as Raspberry Pi, Nvidia Jetson Nano and an Ampere Altra Q64-30 based server. To build HDTN in a native ARM environment add the -DCMAKE_SYSTEM_PROCESSOR flag to the cmake command. This flag removes x86 optimizations and the x86 unit test. Shared libraries are disabled for ARM builds by default.

  • cmake .. -DCMAKE_SYSTEM_PROCESSOR=arm

Run HDTN

Note: Ensure your config files are correct, e.g., The outduct remotePort is the same as the induct boundPort, a consistant convergenceLayer, and the outducts remoteHostname is pointed to the correct IP adress.

You can use tcpdump to test the HDTN ingress storage and egress. The generated pcap file can be read using wireshark.

  • sudo tcpdump -i lo -vv -s0 port 4558 -w hdtn-traffic.pcap

In another terminal, run:

  • ./runscript.sh

Note: The contact Plan which has a list of all forthcoming contacts for each node is located under module/router/contact_plans/contactPlan.json and includes source/destination nodes, start/end time and data rate. Based on the schedule in the contactPlan the Router sends events on link availability to Ingress and Storage. When the Ingress receives Link Available event for a given destination, it sends the bundles directly to egress and when the Link is Unavailable it sends the bundles to storage. Upon receiving Link Available event, Storage releases the bundles for the corresponding destination and when receiving Link Available event it stops releasing the budles.

There are additional test scripts located under the directories test_scripts_linux and test_scripts_windows that can be used to test different scenarios for all convergence layers.

Run Unit Tests

After building HDTN (see above), the unit tests can be run with the following command within the build directory:

  • ./tests/unit_tests/unit-tests

Run Integrated Tests

After building HDTN (see above), the integrated tests can be run with the following command within the build directory:

  • ./tests/integrated_tests/integrated-tests

Routing

The Router module runs by default Dijkstra's algorithm on the contact plan to get the next hop for the optimal route leading to the final destination, then sends a RouteUpdate event to Egress to update its Outduct to the outduct of that nextHop. If the link goes down unexpectedly or the contact plan gets updated, the router will be notified and will recalculate the next hop and send RouteUpdate events to egress accordingly. An example of Routing scenario test with 4 HDTN nodes was added under $HDTN_SOURCE_ROOT/test_scripts_linux/Routing_Test

Bundle Protocol Security (BPSec)

BPSec library module interacts with Ingress and Egress modules and HDTN utility applications such as BPGen/BPSendFile and BPSink/BPReceiveFile to apply and process integrity and confidentiality security services based on the security role. BPSec is enabled by default with Bundle Protocol Version 7 and it requires OpenSSL FIPS module. It can be disabled by setting the CMakeCache.txt variable ENABLE_BPSEC:BOOL to OFF.

Examples of tests with BPSec enabled using intergity and confidentiality services were added under $HDTN_SOURCE_ROOT/test_scripts_linux/BPSec_Tests. BPSec configuration files which include security policy rules and the security operation failure events handling are located under $HDTN_SOURCE_ROOT/config_files/bpsec.

TLS Support for TCPCL Version 4

TLS Versions 1.2 and 1.3 are supported for the TCPCL Version 4 convergence layer. The X.509 certificates must be version 3 in order to validate IPN URIs using the X.509 "Subject Alternative Name" field. HDTN must be compiled with ENABLE_OPENSSL_SUPPORT turned on in CMake. To generate (using a single command) a certificate (which is installed on both an outduct and an induct) and a private key (which is installed on an induct only), such that the induct has a Node Id of 10, use the following command:

  • openssl req -x509 -newkey rsa:4096 -nodes -keyout privatekey.pem -out cert.pem -sha256 -days 365 -extensions v3_req -extensions v3_ca -subj "/C=US/ST=Ohio/L=Cleveland/O=NASA/OU=HDTN/CN=localhost" -addext "subjectAltName = otherName:1.3.6.1.5.5.7.8.11;IA5:ipn:10.0" -config /path/to/openssl.cnf

Note: RFC 9174 changed from the earlier -26 draft in that the Subject Alternative Name changed from a URI to an otherName with ID 1.3.6.1.5.5.7.8.11 (id-on-bundleEID).

  • Therefore, do NOT use: -addext "subjectAltName = URI:ipn:10.0"
  • Instead, use: -addext "subjectAltName = otherName:1.3.6.1.5.5.7.8.11;IA5:ipn:10.0"

To generate the Diffie-Hellman parameters PEM file (which is installed on an induct only), use the following command:

  • openssl dhparam -outform PEM -out dh4096.pem 4096

Audio and Video Streaming

HDTN has media streaming applications BpSendStream and BpRecvStream. BpSendStream intakes an RTP stream from an outside source, bundles RTP packets, and sends the bundles to a BP network. BpRecvStream performs the inverse operation by intaking a stream of bundles, extracting the RTP packets, and forwarding the RTP stream to an IP network. Once built, the bpsend_stream executable can be used to ingest media such as RTP packets and mp4 files, and send them to a DTN network as bundles over any supported convergence layer. Similarly, the bprecv_stream executable can be used to receive bundles from a DTN and reassemble the media.

Streaming is disabled by default. It can be enabled by setting the CMakeCache.txt variable ENABLE_STREAMING_SUPPORT:BOOL to ON.

Examples of File Streaming tests were added under $HDTN_SOURCE_ROOT/test_scripts_linux/Streaming. Before running the tests set the HDTN_SOURCE_ROOT the hdtn root directory and LD_LIBRARY_PATH to /usr/local/lib/x86_64-linux-gnu

Streaming dependencies need also to be installed. See below example of Gstreamer dependencies installation steps.

Streaming Dependencies installation (Gstreamer)
  1. Install OS dependencies
sudo apt-get update -y && sudo apt-get install -y pkg-config \
    flex bison git python3-pip ninja-build libx264-dev \
    cmake build-essential libzmq3-dev libboost-dev libboost-all-dev openssl libssl-dev
  1. Install meson and the directory where it was installed to PATH
sudo pip3 install meson
  1. Clone, build, and install Gstreamer
git clone https://github.com/GStreamer/gstreamer.git 

cd gstreamer && meson setup build 

meson configure build && meson compile -C build 

ninja -C build install

An alternative way to install all these dependencies:

sudo aptitude install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio
Viewing Multimedia Streams

The following command can be used to view video for H264 Stream:

gst-launch-1.0 -v -e udpsrc address=127.0.0.1 port=8989 ! "application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264, payload=(int)96" ! rtph264depay ! queue ! h264parse ! decodebin ! autovideosink

The following command can be used to view video with audio for H264 Stream:

gst-launch-1.0 -v -e udpsrc address=127.0.0.1 port=8989 ! "application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)MP2T, payload=(int)96" ! rtpmp2tdepay ! queue ! tsparse ! decodebin ! autovideosink
Displaying received File Stream

The following command can be used to play the received video file to compare it to the original file

ffplay <name of file>

Web User Interface

This repository comes equiped with code to launch a web-based user interface to display statistics for the HDTN engine. It relies on a dependency called Boost Beast which is packaged as a header-only library that comes with a standard Boost installation. The web interface will use OpenSSL (if found by CMake) since the web interface supports both http as well as https, and hence both ws (WebSocket) and wss (WebSocket Secure). If OpenSSL is not found, the web interface will only support http/ws. The web user interface is enabled by default at compile time. If the web user interface is not desired, it can be turned off by setting the CMakeCache.txt variable USE_WEB_INTERFACE:BOOL to OFF. The web interface can be built with CivetWeb (statically linked without SSL support) instead of Boost Beast in order to reduce HDTN binary file size. The HDTN CMake will automatically download and build CivetWeb and link to it statically. Simply set CMake cache option WEB_INTERFACE_USE_CIVETWEB to ON (or run cmake with argument -DWEB_INTERFACE_USE_CIVETWEB:BOOL=ON). If you are building without access to the internet, you can download version 1.16 source zip file of CivetWeb from GitHub and set the CMake cache variable CIVETWEB_PREDOWNLOADED_ZIP_FILE to point to the filesystem path of the downloaded zip file.

Now anytime that HDTNOneProcess runs, the web page will be accessible at http://localhost:8086 and an alternative "system view" gui based on D3.js will be accessible at http://localhost:8086/hdtn_d3_gui/

Beta 2.0 Web User Interface

There is a new Web User Interface under development which leverages a modern, single-page-app web framework. The existing Web User Interface is in the process of being migrated over to the new framework and can be used in a Beta capacity if desired. The following are functional components in the new GUI:

  • Configuration - Allows you to generate HDTN configuration files in a web form

Building

Dependencies

The new Web User Interface leverages Node Package Manager for handling dependencies and building the web files to host. Download Node before attempting to build.

Build Steps

HDTN needs to be configured to host the new UI. First set the GUI version environment variable:

export HDTN_GUI_VERSION=2

At this point HDTN needs to be rebuilt, re-run the cmake and make commands. If Node Package Manager is installed correctly and HDTN_GUI_VERSION is set to 2, cmake will automatically build and host the new GUI. After HDTN is rebuilt, simply start HDTN as normal, the GUI will continue to be accessible via a web browser at http://localhost:8086.

Simulations

HDTN can be simulated using DtnSim, a simulator for delay tolerant networks built on the OMNeT++ simulation framework. Use the "support-hdtn" branch of DtnSim which can be found in the official DtnSim repository. Currently HDTN simulation with DtnSim has only been tested on Linux (Debian and Ubuntu). Follow the readme instructions for HDTN and DtnSim to install the software. Alternatively, a pre-configured Ubuntu VM is available for download here (the username is hdtnsim-user and password is grc). More Details about the installation steps can be found here

Docker Instructions

First make sure docker is installed.

apt-get install docker

Check the service is running

systemctl start docker

There are currently two Dockerfiles for building HDTN, one for building an Oracle Linux container and the other for building an Ubuntu. This command will build the Ubuntu one:

docker build  -t hdtn_ubuntu containers/docker/ubuntu/.

The -t sets the name of the image, in this case hdtn_ubuntu. Check the image was built with the command:

docker images

Now to run the container use the command:

docker run -d -t hdtn_ubuntu

Check that it is running with:

docker ps

To access it, you'll need the CONTAINER_ID listed with the ps command

docker exec -it container_id bash

Stop the container with

docker stop container_id

The same container can either be restarted or removed. To see all the containers Use:

docker ps -a

These can still be restarted with the run command above. To remove one that will no longer be used:

docker rm container_id

Docker Compose Instructions

Docker compose can be used to spin-up and configure multiple nodes at the same time. This is done using the docker compose file found under HDTN/containers/docker/docker_compose

cd containers/docker/docker_compose

This file contains instructions to spin up two containers using Oracle Linux. One is called hdtn_sender and the other hdtn_receiver. Start them with the following command:

docker compose up

On another bash terminal these can be accessed using the command:

docker exec -it hdtn_sender bash
docker exec -it hdtn_receiver bash

This setup is perfect for running a test between two hdtn nodes. An example script for each node can be found under HDTN/tests/test_scripts_linux/LTP_2Nodes_Test/. Be sure to run the receiver script first, otherwise the sender will have nowhere to send to at launch.

Kubernetes Instructions

Download the dependencies

sudo apt-install docker microk8s

The first step is to create a docker images to be pushed locally for kubernetes to pull:

docker build docker/ubuntu/. -t myhdtn:local

Check that it was built:

docker images

Next we build the image locally and inject it into the microk8s image cache

docker save myhdtn > myhdtn.tar
microk8s ctr image import myhdtn.tar

Confirm this with:

microk8s ctr images ls

Now we deploy the cluster, the yaml must reference the injected image name

microk8s kubectl apply -f containers/kubernetes/hdtn_10_node_cluster.yaml

There should now be ten kubernetes pods running with HDTN. See them with:

microk8s kubectl get pods

To access a container in a pod, enter the following command:

microk8s kubectl exec -it container_name -- bash

When you're finished working with this deployment, delete it using:

microk8s kubectl delete deployment hdtn-deployment

Use the get pods command to confirm they've been deleted

microk8s kubectl get pods