Left Side: User-Defined Configurator (Orange)
This is the user-facing interface where researchers define their emulation scenarios:
- Emulation Parameters: Users specify constellation details (satellite constellation type, network topology, node configurations including satellites, ground stations, and user equipment)
- Emulation Rules: Users define dynamic behaviors like:
- ISL (Inter-Satellite Link) failure models
- GSL (Ground-Satellite Link) handover policies
- Trajectory updates as satellites orbit
- Topology Configurator: Processes these inputs and translates them into actionable configuration data
Center: Container Network Manager (Blue)
This is the core engine that manages the actual emulation infrastructure:
-
Container Runtime Manager:
- Creates and manages Docker containers representing satellites and ground stations
- Handles resource allocation and container lifecycle management
- Uses parallel processing to speed up container operations
-
Virtual Link Manager:
- Creates and manages virtual network links between satellites (ISLs) and between satellites and ground stations (GSLs)
- Implements the innovative eBPF-based link management for improved performance
- Handles dynamic link state changes as satellites move
-
Message Forwarder:
- Facilitates communication between the Key-Value Database and the running containers
- Ensures real-time updates reach the appropriate network nodes
Right Side: Multi-Machine Emulation Environment (Green)
This shows the actual virtualized satellite network in operation:
- Distributed Containers: Each blue box represents a container running satellite or ground station software
- VXLAN Connectivity: The network uses VXLAN (Virtual Extensible LAN) technology to connect containers across multiple physical machines
- Container Applications: Each container runs real networking software like routing protocols (e.g., FRRouting, BIRD)
- Scalable Grid: The 3x3 grid pattern indicates how the system can scale across multiple machines
Bottom: Key-Value Database (Gray)
This serves as the central information hub:
- Machine Information: Tracks which physical machines are part of the emulation cluster
- Node Data: Stores details about each satellite and ground station
- Link Information: Maintains current state of all network connections
- Interface Details: Tracks network interface configurations
- Separation Architecture: Acts as a buffer between user configuration and the actual network implementation, enhancing flexibility
Key Architectural Innovations
Bidirectional Arrows: The double-headed arrows show that information flows both ways - users can monitor the emulation state in real-time while making dynamic configuration changes.
Separation Design: Unlike other platforms where user configuration directly controls containers, OpenSN uses the Key-Value Database as an intermediary. This allows:
- Multiple users to interact with the same emulation
- Dynamic reconfiguration without stopping the emulation
- Better extensibility for adding new features
Multi-Machine Support: The right side shows how containers can be distributed across multiple physical machines, connected via VXLAN, enabling much larger constellation emulations than single-machine platforms.
Container-Based Approach: Unlike Mininet-based tools that use lightweight processes, OpenSN uses full Docker containers, allowing each satellite to run complete, unmodified networking software stacks.
This architecture enables OpenSN to achieve its demonstrated performance improvements: 5-10x faster constellation construction than StarryNet and 2-4x faster link updates than LeoEM, while supporting mega-constellations with thousands of satellites across multiple machines.
Chinese researchers unveil OpenSN
groundbreaking emulation platform that could accelerate innovation in the rapidly expanding field of Low Earth Orbit satellite networks
Researchers at Beihang University in China have developed a powerful new open-source tool that promises to transform how scientists and engineers test and develop technologies for Low Earth Orbit (LEO) satellite networks like Starlink. The tool, called OpenSN, offers unprecedented speed and scalability for emulating massive satellite constellations, potentially accelerating research in a field critical to the future of global internet connectivity.
Breaking Performance Barriers
OpenSN represents a significant advancement over existing satellite network emulation tools, achieving construction speeds 5-10 times faster than StarryNet, a leading container-based emulator, and link state updates 2-4 times faster than LeoEM, a popular Mininet-based platform. Most impressively, the research team successfully demonstrated OpenSN's capabilities by emulating the complete five-shell Starlink constellation with 4,408 satellites - a feat that showcases the platform's ability to handle real-world mega-constellation scenarios.
The tool adopts container-based virtualization, which allows for running distributed routing software on each node and achieves horizontal scalability via flexible multi-machine extension, distinguishing it from Mininet-based emulators like LeoEM. This architectural choice enables researchers to run realistic networking protocols and applications across thousands of virtual satellites.
Addressing Critical Research Needs
The timing of OpenSN's release is particularly significant given the explosive growth in LEO satellite deployments. SpaceX's Starlink constellation alone consists of over 7,600 mass-produced small satellites as of May 2025, with plans for nearly 12,000 satellites and a possible extension to 34,400. If current commercial satellite plans are realized, more than 65,000 satellites could be circling Earth within the next decade, with some proposals calling for 500,000 satellites across multiple mega-constellations.
"It is a crucial problem how to evaluate these studies in a systematic and reproducible manner," the researchers noted in their paper, highlighting the need for better emulation platforms as LEO satellite networks become an essential component of the future Internet.
Technical Innovations
OpenSN incorporates several key innovations that set it apart from existing tools:
Enhanced Efficiency: The platform streamlines interaction with Docker command line interface and significantly reduces unnecessary operations during virtual link creation, improving both emulation efficiency and vertical scalability on single machines.
Separation Architecture: OpenSN separates user-defined configuration from container network management through a Key-Value Database that records necessary information for satellite network emulation, enhancing functional extensibility.
eBPF Integration: The platform incorporates advanced eBPF (extended Berkeley Packet Filter) technology to improve the efficiency of link management and handover operations, particularly important for simulating the dynamic nature of satellite networks.
Multi-Machine Scalability: The platform supports deployment across multiple machines, enabling researchers to distribute computational load and emulate larger constellations than possible on single systems.
Real-World Applications and Impact
The research team demonstrated OpenSN's practical capabilities through various scenarios, including emulating satellite-terrestrial integrated networks for video streaming applications. The platform supports customization for different constellation scales and topology types, with automation scripts to generate configuration files for large-scale simulations.
Growing Research Ecosystem
OpenSN enters a competitive landscape of satellite network research tools. LeoEM, developed at UC San Diego, provides real-time LEO satellite network emulation with high observability, allowing researchers to run native programs over dynamic satellite links. Meanwhile, other platforms like StarryNet and the recently introduced xeoverse offer alternative approaches to mega-constellation simulation, each with unique strengths in scalability and real-time performance.
Evaluation studies show that xeoverse, another recent platform, achieves 2.9 times faster simulation than Hypatia and 40 times faster than StarryNet in certain scenarios, highlighting the competitive nature of this research area.
Environmental and Safety Considerations
The research comes at a time of growing concern about the environmental and safety implications of mega-constellations. Scientists warn that at peak deployment, Starlink's constellation alone could require 29 tons of satellites to enter Earth's atmosphere daily for replacement, raising questions about atmospheric pollution and space debris.
Recent research has also revealed instability issues in self-driving satellite mega-constellations, where safety-oriented collision avoidance maneuvers can create cascading effects that threaten both network lifetime and capacity. These findings underscore the importance of sophisticated simulation tools like OpenSN for understanding and mitigating such risks.
Research Team and Availability
The OpenSN project was developed by researchers Wenhao Lu, Zhiyuan Wang, Hefan Zhang, Shan Zhang, and Hongbin Luo from Beihang University's School of Computer Science and Engineering. The work was presented at the 8th Asia-Pacific Workshop on Networking (APNet'24) in Sydney, Australia, in August 2024.
OpenSN is available as open-source software at https://opensn-library.github.io, enabling researchers worldwide to contribute to and benefit from this advancing platform.
Looking Forward
As the space economy continues to expand and LEO satellite networks become increasingly critical infrastructure, tools like OpenSN will play a vital role in ensuring these systems are reliable, efficient, and safe. The platform's ability to handle real-world scale simulations positions it as a valuable resource for both academic researchers and industry developers working on next-generation satellite networking technologies.
The success of OpenSN also highlights China's growing prominence in space technology research, complementing the country's expanding satellite deployment programs and contributing to the global effort to understand and optimize mega-constellation operations.
SIDEBAR: Satellite Network Emulation Platforms Comparison
The rapidly evolving field of LEO satellite network research has spawned several competing emulation platforms, each with distinct advantages and limitations. Here's how the major platforms compare:
Performance and Scalability Comparison
|
Platform |
Developer |
Architecture |
Max Satellites Tested |
Construction Speed |
Key Strengths |
Limitations |
|
OpenSN |
Beihang University (2024) |
Container-based |
4,408 (5-shell Starlink) |
5-10x faster than StarryNet |
Multi-machine scaling, eBPF optimization, separation architecture |
Newer platform, limited real-world testing |
|
StarryNet |
Tsinghua University (2023) |
Container-based |
~1,023 (Docker limit) |
Baseline |
Real protocol stacks, distributed routing |
Docker bridge limitations, slower performance |
|
LeoEM |
UC San Diego (2023) |
Mininet-based |
~720 (OneWeb scale) |
Fast for simple scenarios |
Real-time observability, native applications |
No distributed routing, single-machine only |
|
xeoverse |
Recent research (2024) |
Mininet + lightweight VMs |
1,584+ (Starlink Shell 1) |
2.9x faster than Hypatia |
Real-time simulation, weather modeling |
Limited to specific scenarios |
|
Hypatia |
ETH Zurich (2020) |
ns3 discrete-event |
Large-scale |
Slower due to events |
Comprehensive modeling, established |
Simulation-only, no real protocols |
Technical Capabilities Matrix
|
Feature |
OpenSN |
StarryNet |
LeoEM |
xeoverse |
Hypatia |
|
Virtualization Type |
Docker containers |
Docker containers |
Mininet (processes) |
Mininet + VMs |
Discrete-event simulation |
|
Multi-machine Support |
✅ Yes |
❌ Limited |
❌ No |
❌ No |
❌ No |
|
Real Protocol Stacks |
✅ Yes |
✅ Yes |
✅ Yes |
✅ Yes |
❌ Simulated |
|
Distributed Routing |
✅ FRRouting/BIRD |
✅ BIRD |
❌ No |
✅ Yes |
❌ Simulated |
|
eBPF Optimization |
✅ Yes |
❌ No |
❌ No |
❌ No |
❌ N/A |
|
Real-time Operation |
✅ Yes |
✅ Yes |
✅ Yes |
✅ Yes |
❌ Faster than real-time |
|
Link Handover |
✅ Optimized |
✅ Basic |
✅ Basic |
✅ Yes |
✅ Modeled |
|
Weather Effects |
❌ Limited |
❌ No |
❌ No |
✅ Yes |
❌ No |
Performance Benchmarks
Network Construction Speed (relative to baseline):
- OpenSN: 5-10x faster than StarryNet
- LeoEM: Fastest for basic scenarios (no routing software)
- xeoverse: 40x faster than StarryNet in specific tests
- StarryNet: Baseline reference
- Hypatia: 5x slower than xeoverse for updates
Link State Updates:
- OpenSN: 2-4x faster than LeoEM
- OpenSN with eBPF: 10x faster for handovers
- LeoEM: Good for simple topology changes
- StarryNet: Bottlenecked by Docker operations
Scalability Limits:
- OpenSN: Demonstrated up to 4,408 satellites
- StarryNet: Limited to ~1,023 satellites (Docker bridge constraint)
- LeoEM: Tested up to 720 satellites (OneWeb constellation)
- xeoverse: 1,584+ satellites (Starlink Shell 1)
- Hypatia: Theoretically unlimited but performance degrades
Resource Requirements
|
Platform |
Memory Usage |
CPU Efficiency |
Storage |
Network Overhead |
|
OpenSN |
Moderate |
High (parallel processing) |
Standard |
Optimized (eBPF) |
|
StarryNet |
High (Docker overhead) |
Moderate |
High |
High (CLI operations) |
|
LeoEM |
Low |
High (no routing) |
Low |
Low |
|
xeoverse |
Moderate |
High |
Moderate |
Moderate |
|
Hypatia |
Low |
Variable |
Low |
Minimal |
Use Case Recommendations
Choose OpenSN for:
- Large-scale constellation research (1000+ satellites)
- Multi-machine distributed testing
- Performance-critical applications
- Advanced routing protocol development
Choose StarryNet for:
- Mid-scale testing with real protocols
- Established research requiring compatibility
- Applications needing distributed routing
Choose LeoEM for:
- Rapid prototyping and testing
- Real-time application evaluation
- Single-machine development environments
Choose xeoverse for:
- Weather impact studies
- Real-time simulation requirements
- Starlink-specific research
Choose Hypatia for:
- Large-scale theoretical modeling
- Protocol simulation without implementation
- Academic research with extensive analysis
Development Status and Availability
- OpenSN: Actively developed, open-source at https://opensn-library.github.io
- StarryNet: Available at https://github.com/SpaceNetLab/StarryNet
- LeoEM: Available at https://github.com/XuyangCaoUCSD/LeoEM
- xeoverse: Research prototype, limited availability
- Hypatia: Open-source, mature platform
Note: Performance comparisons are based on published benchmarks and may vary depending on specific hardware configurations and test scenarios.
Sources
- Lu, W., Wang, Z., Zhang, H., Zhang, S., & Luo, H. (2025). OpenSN: An Open Source Library for Emulating LEO Satellite Networks. IEEE Transactions on Parallel and Distributed Systems, 36(8), 1574-1590. DOI: 10.1109/TPDS.2025.3575920
- Lu, W., Wang, Z., Zhang, S., Meng, Q., & Luo, H. (2024). OpenSN: An Open Source Library for Emulating LEO Satellite Networks. Proceedings of the 8th Asia-Pacific Workshop on Networking, 149-155. https://dl.acm.org/doi/10.1145/3663408.3663430
- Cao, X., & Zhang, X. (2023). LeoEM: A real-time LEO satellite network emulator. GitHub repository. https://github.com/XuyangCaoUCSD/LeoEM
- Lai, Z., et al. (2023). StarryNet: Empowering Researchers to Evaluate Futuristic Integrated Space and Terrestrial Networks. USENIX Symposium on Networked Systems Design and Implementation. https://github.com/SpaceNetLab/StarryNet
- OpenSN Library. (2024). OpenSN-Library GitHub Organization. https://github.com/OpenSN-Library
- SpaceX. (2025). Starlink Technology and Updates. https://www.starlink.com/technology
- Wikipedia. (2025). Starlink satellite constellation. https://en.wikipedia.org/wiki/Starlink
- Britannica. (2023). Megaconstellation satellite networks. https://www.britannica.com/technology/megaconstellation
- Chen, Y., et al. (2024). Instability of Self-Driving Satellite Mega-Constellation: From Theory to Practical Impacts on Network Lifetime and Capacity. arXiv preprint arXiv:2406.06068. https://arxiv.org/abs/2406.06068
- PIRG Education Fund. (2025). Environmental harms of satellite internet mega-constellations. https://pirg.org/edfund/resources/wastex-environmental-harms-of-satellite-internet-mega-constellations/
- Lawler, S. (2024). What goes up must come down: How megaconstellations pose safety threats. Live Science. https://www.livescience.com/space/astronomy/what-goes-up-must-come-down-how-megaconstellations-like-spacexs-starlink-network-pose-a-grave-safety-threat-to-us-on-earth-opinion
- Satellite Open Source Collection. (2024). GitHub repository for satellite communication research. https://github.com/jwwthu/Satellite-Open-Source
Sources
-
Lu, W., Wang, Z., Zhang, H., Zhang, S., & Luo, H. (2025). OpenSN: An Open Source Library for Emulating LEO Satellite Networks. IEEE Transactions on Parallel and Distributed Systems, 36(8), 1574-1590. DOI: 10.1109/TPDS.2025.3575920
-
Lu, W., Wang, Z., Zhang, S., Meng, Q., & Luo, H. (2024). OpenSN: An Open Source Library for Emulating LEO Satellite Networks. Proceedings of the 8th Asia-Pacific Workshop on Networking, 149-155. https://dl.acm.org/doi/10.1145/3663408.3663430
-
Cao, X., & Zhang, X. (2023). LeoEM: A real-time LEO satellite network emulator. GitHub repository. https://github.com/XuyangCaoUCSD/LeoEM
-
Lai, Z., et al. (2023). StarryNet: Empowering Researchers to Evaluate Futuristic Integrated Space and Terrestrial Networks. USENIX Symposium on Networked Systems Design and Implementation. https://github.com/SpaceNetLab/StarryNet
-
OpenSN Library. (2024). OpenSN-Library GitHub Organization. https://github.com/OpenSN-Library
-
SpaceX. (2025). Starlink Technology and Updates. https://www.starlink.com/technology
-
Wikipedia. (2025). Starlink satellite constellation. https://en.wikipedia.org/wiki/Starlink
-
Britannica. (2023). Megaconstellation satellite networks. https://www.britannica.com/technology/megaconstellation
-
Chen, Y., et al. (2024). Instability of Self-Driving Satellite Mega-Constellation: From Theory to Practical Impacts on Network Lifetime and Capacity. arXiv preprint arXiv:2406.06068. https://arxiv.org/abs/2406.06068
-
PIRG Education Fund. (2025). Environmental harms of satellite internet mega-constellations. https://pirg.org/edfund/resources/wastex-environmental-harms-of-satellite-internet-mega-constellations/
-
Lawler, S. (2024). What goes up must come down: How megaconstellations pose safety threats. Live Science. https://www.livescience.com/space/astronomy/what-goes-up-must-come-down-how-megaconstellations-like-spacexs-starlink-network-pose-a-grave-safety-threat-to-us-on-earth-opinion
-
Satellite Open Source Collection. (2024). GitHub repository for satellite communication research. https://github.com/jwwthu/Satellite-Open-Source
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