AWS in Orbit: Automated Satellite Management – Detailed Summary

Published on April 19, 2025, as part of the "AWS in Orbit" series by N2K Networks’ T-Minus Space Daily, this episode delves into the innovative realm of automated satellite management. Hosted by Maria Varmazes, the episode features insightful discussions with Dax Garner, CTO of Cognitive Space, and Ed Miletian, Solutions Architect at AWS Aerospace and Satellite. Below is a comprehensive summary capturing the key points, discussions, insights, and conclusions from the episode.

1. Introduction to Participants and Their Roles

Maria Varmazes opens the episode by introducing the guests:

• Dax Garner – Chief Technology Officer at Cognitive Space, with a strong background in aerospace engineering, including experience at NASA Johnson Space Center and Lockheed Martin. Dax emphasizes his passion for leveraging AI and machine learning (ML) to facilitate space infrastructure development.

  “Space is hard and AI and ML technologies can really make it easier. And that's a key component in getting infrastructure and eventually humans into space.” [03:06]

• Ed Miletian – Solutions Architect at AWS Aerospace and Satellite, specializing in bridging cloud solutions with aerospace missions. Ed brings experience from various missions with NASA and Space Force, focusing on mission management and planning.

  “I'm excited to talk with y'all today about it.” [02:06]

2. Cognitive Space: Mission and Solutions

Cognitive Space's Mission: Dax highlights the company's focus on empowering space assets through advanced AI and ML algorithms, particularly for managing proliferated satellite systems.

• Proliferated Systems: Refers to large constellations of satellites working in unison to perform missions such as Earth imaging or establishing global mesh networks. Managing these systems involves complex optimization challenges.

  “When you have a huge combinatorial space, so many options, it can become very difficult to optimize that effectively with traditional constraint based or other operations research type algorithms.” [05:21]

Problem Statement: Traditional optimization methods struggle with the vast combinatorial possibilities in satellite management, often resulting in suboptimal mission performance or excessive computation times.

• Heuristics vs. ML Solutions: While heuristics offer quick solutions, they lack optimality. Cognitive Space leverages ML models to achieve near-optimal solutions with speeds comparable to heuristics, thus enhancing mission efficiency without significant delays.

  “The sweet spot is training and designing ML models that can run at the speeds of heuristics... you can buy back a lot of that optimality.” [06:47]

3. Applications and Real-World Impact

Key Applications:

• Space Development Agency (SDA): Optimizing link management within their mesh network constellation, ensuring efficient communication between satellites even when nodes fail.

• National Geospatial-Intelligence Agency (NGA) and National Reconnaissance Office (NRO): Enhancing geospatial request handling by predicting and managing the capacity of commercial satellite providers.

  “We can help them understand that capacity and make predictions about whether they can fulfill certain requests on the commercial side.” [08:07]

Performance Metrics:

• Cognitive Space's ML algorithms achieve 90-95% of optimal solutions, significantly outperforming traditional heuristics which operate at 50-60% optimality.

  “We're really getting back to like 90, 95% of the optimum solutions, depending on the objective and the constraints.” [12:04]

• Operational Efficiency: Capable of planning for hundreds of satellites within minutes, facilitating real-time adjustments to dynamic mission requirements.

  “We're planning in minutes. You're planning hundreds of satellites in minutes...” [12:04]

4. The Role of AWS in Enabling Automated Satellite Management

AWS’s Infrastructure Support:

• Elastic Kubernetes Service (EKS) and Elastic Container Service (ECS): Cognitive Space utilizes these AWS services to run ML algorithms and deploy applications within scalable cloud environments.

  “Cognitive Space uses EKs and ECSs in terms of running all of our ML algorithms and standing up our applications in their cloud environments.” [10:33]

Scalability and Flexibility:

• The chosen architecture allows Cognitive Space to scale their mission planning and onboarding processes effortlessly, accommodating new constellations and missions without extensive reengineering.

  “It scales really well to train the models and also to execute the models when you're planning.” [10:46]

Security and Compliance:

• AWS ensures that services comply with stringent security requirements, supporting operations in both GovCloud and highly classified regions, which is critical for national security clients.

  “These services all can run in both our GovCloud regions as well as our most classified secret and top secret regions.” [11:40]

5. Future Perspectives: AI and Agency in Satellite Management

Advancements in AI:

• Agentic Systems: Dax discusses integrating ML models with agentic systems that can dynamically select and apply the most appropriate optimization models based on real-time constraints and objectives.

  “When you combine those models, those optimization models, and pair them with an agentic system, an agent gets to decide...” [15:54]

• Adaptive Objective Functions: The ability to shift from constraints to objectives allows for more flexible and responsive mission planning, accommodating unforeseen changes like hardware failures or urgent mission requirements.

AWS’s Mission Operations Center:

• Cloud-Based Operations: Ed elaborates on AWS’s concept of a Mission Operations Center, which leverages cloud scalability to run various mission subsystems, ensuring continuous access to cutting-edge infrastructure without the burden of hardware management.

  “With the cloud Mission Operations center, you are automatically getting the best available technology under the hood...” [16:29]

6. Conclusion and Future Collaborations

Growth of Satellite Constellations:

• Both guests agree that the proliferation of satellite constellations is an undeniable trend. AWS aims to alleviate the infrastructural burdens on customers, allowing them to focus on mission objectives while AWS provides scalable support.

  “Constellations are going to keep increasing. That is just a trend. That is absolutely true.” [18:12]

Empowering the Space Economy:

• Dax emphasizes Cognitive Space's commitment to supporting not just government clients but also startups entering the space arena. By providing scalable management systems, Cognitive Space enables emerging companies to manage their first satellites and scale up seamlessly.

  “We're very focused on the US Government at the moment... but the key is to enable an entire space economy.” [18:12]

AWS and Cognitive Space Partnership:

• The collaboration between AWS and Cognitive Space is poised to drive the future of automated satellite management, offering robust, scalable, and secure solutions essential for modern space missions.

  “AWS is gonna be focused on reducing that burden on customers so they can focus on mission...” [18:12]

Notable Quotes

• Dax Garner on Mission-Driven AI in Space:

  “Space is hard and AI and ML technologies can really make it easier.” [03:06]

• Ed Miletian on Cloud Scalability:

  “You're always getting access to the best underlying infrastructure.” [16:29]

• Maria Varmazes on Future Prospects:

  “The future I think in our collaboration for sure.” [19:52]

Final Thoughts: This episode of "AWS in Orbit" underscores the transformative role of AI and cloud technologies in managing the ever-growing fleet of satellites. Through the collaboration between Cognitive Space and AWS, the space industry is poised to achieve unprecedented levels of efficiency, scalability, and adaptability, paving the way for future innovations in space exploration and infrastructure management.