Home / Technology / AI & IT / Satellite-based cloud computing deploying Datacenter on Satellites for Edge and Fog Computing

Satellite-based cloud computing deploying Datacenter on Satellites for Edge and Fog Computing

Constellations of satellites are being proposed in large numbers; most of them are expected to be in orbit within the next decade. They will provide communication to unserved and underserved communities, enable global monitoring of Earth and enhance space observation.


Constellations have their greatest potential in the communication field. The upcoming era of the Internet-Of-Things requires the communication infrastructure to handle huge amounts of data and to guarantee service in any geographical position. Constellations, however, also have great potential in weather science, safety/security and disaster monitoring.


As the number of satellites continues to increase, satellites become an important part of the IoT and 5G/6G communications. How to deal with the data of the satellite Internet of Things is a problem worth considering and paying attention to. Several space startups are integrating micro-data centers into their designs, offering computing power to process satellite imaging data or monitor distributed sensors for Internet of Things (IoT) applications.


Edge computing requirements

The number of remote sensing satellites has increased dramatically in satellite launches in recent years. After acquiring the remote sensing image data of the satellite, the researchers used the artificial intelligence algorithm and the powerful computing power of the ground data center to extract the hidden information in the remote sensing image. However, researchers need to spend a lot of time and cost to complete this process. In the existing satellite communications, most of the observation, relay, and communication satellites are single-star and single-chain, and there is no network. Due to the limitation of energy consumption, the available processors on the satellites have poor performance and cannot meet the growing demand for space computing tasks.


At the same time, the satellite communication rate between satellites and other satellites, and between satellites and the ground, is generally not improved. The amount of data generated by the on-board sensor is large, causing a high delay in the data transmission process, which is very disadvantageous for scenes with high real-time requirements (such as early warning).


The demand for processing data close to its origin led to increased popularity of the edge computing paradigm in research and industry. The main idea behind edge computing is to embed computing resources into the edge of the network, i.e., close to clients. Edge Computing is a Distributed Computing Model when computation takes place near a location where data is collected and analyzed, rather than on a Centralized Server or in the Cloud.  Compared to cloud computing, resources are thus available with low latency and bandwidth costs. For space-based systems, edge computing can save both time and energy. Energy is an even more precious resource in space than on Earth, so cutting down on transmissions, whether to relay information or run equations, can be hugely important. Preventing data from being transmitted to the cloud can also reduce privacy and security risks.


For example, sensor-equipped swarms of nanosatellites, such as ChipSats and CubeSats, can use edge systems to process the data they collect in low-Earth orbit without activating the satellite’s power-hungry radio. These satellite swarms, which fly about 250 to 370 miles above the Earth’s surface, can be clustered and organized to support important missions in the study of weather, climate science, national security and disaster response.


Fog and Edge Computing implementations

Satellite communication systems are flexible enough to be adapted for the implementation of Fog and Edge Computing. Implementation of Edge Computing in satellite telecommunication systems can be ensured by supplementing of the User Terminal or VSAT Terminal Modem with an additional Computing Module or Single-Board Computer. Structurally, a User Terminal or VSAT Terminal is a board with modem chips installed on it. Through modernization, such a design can be supplemented with a Single-Board Computer, which will provide the implementation of Edge Computing.


An alternative option is to connect a Single-Board Computer to an Ethernet-type Local Area Network with a Wi-Fi router being connected to it as well as other equipment of radio access technology for short-range IoT Smart Things. This added Computing Capacity will support the IoT Smart Things computing needs within the coverage of a short-range radio access network. In this case, only the results information about the IoT local information processing will be transmitted via a satellite communication channel.


One of the proposed architectures is a hierarchical network of satellite IoT supported by terrestrial data centers. Specifically, the cloud-edge stratified satellite IoT edge computing system consists of three parts: the satellite IoT cloud node, the satellite IoT edge node, and the ground data center. The satellite IoT edge nodes have computing and storage capabilities and use a common virtualization platform that can deploy different services as needed. Satellite IoT edge nodes can communicate with each other, and satellite IoT edge nodes and satellite IoT cloud nodes can cooperate with each other.


This can bring two benefits. First, satellite IoT edge nodes can request assistance from satellite IoT cloud nodes or ground data centers to offload their computing tasks to them. Second, satellite IoT edge nodes can also accept tasks from satellite IoT cloud nodes or terrestrial data centers, or establish fast service clusters with other satellite IoT edge nodes.


The implementation of Fog Computing in the satellite segment of IoT Systems is possible by supplementing the orbital segment with Computing Capacity for the Fog computing implementation. Supplementing the Orbital Segment of Satellite Communications Systems with Computing Capacity will allow the implementation of Fog computing for processing of the IoT Information accepted from IoT Smart Things located in the service area of the Satellite. As a result, the efficiency of information processing will increase, and the Delay Time will be reduced.


An alternative solution is the development and launch of GEO Satellite, with a Cloud Data Center Module as a Payload. These Satellites will be accessed via GEO Satellite-Repeaters according with Inter-Satellite Links. To increase data storage and computing operations liability, to increase cloud computing productivity, Satellite Cloud Computing Data Centers will be connected to ground-based Cloud Computing Data Centers provided with special high-speed secure radio links.


Satellite IoT cloud nodes have more powerful computing and storage capabilities than satellite IoT edge nodes. Satellite IoT cloud nodes are equipped with heterogeneous resources, such as CPU, GPU, and FPGA. It can not only handle various applications unloaded from satellite IoT edge nodes, but also complete task scheduling, task analysis, data fusion, intelligent distribution, and fast service cluster construction of the entire satellite network.


The ground data center has the capability of a large cloud computing center that can communicate with satellite IoT nodes or the ground Internet. Compared to satellite IoT nodes, the ground data centers have the highest computing power and the most storage resources.


Challenges for LEO edge

The highly dynamic nature of satellite constellations and their limited capacity for computational power means that existing edge computing platforms are not yet ready for being applied to the LEO edge.


Mobile Server Infrastructure. The servers attached to satellites in a LEO constellation is that these satellites orbit the earth at high speeds. For example, a satellite at an altitude of 550km must maintain a speed of 27,000km/h to maintain its orbit. Consequently, the servers also move at this speed. For the static ground station equipment, this means that they must frequently change their communication partner.


Same-model Servers. Then, satellites in a constellation are mostly the same model. The reason for this is that satellites orbit the earth continuously while the earth revolves beneath the satellite constellation. Thus, each satellite eventually covers each part of the earth which means that using different kinds of satellites for different regions is not possible. Subsequently, the servers must also be of the same model. It can be possible to upgrade server capabilities over time as satellites reach the end of their lifetime, yet developing different versions can have a negative impact on development and production costs.


Homogeneously Distributed Servers. Due to their non-geostationary nature, satellites are also homogeneously distributed across the globe, with satellites evenly spaced across an orbit. This means that each ground station has access to more or less the same amount of equally equipped satellites at all times.


Heterogeneous Demand. Nevertheless, demand is of course not homogenous across earth. Urban areas have a higher client density which increases resource demand compared to rural areas or oceans with a smaller client population.


Limited Compute Capabilities. As a consequence of being deployed in space, satellite servers’ capabilities must be limited. The reason for this is that energy consumption and heat generation must be kept low for economical reasons. Larger heat dissipation mechanisms, batteries, or solar arrays lead to higher weight and, subsequently, higher launch costs.


Another effect of placing servers on satellites in LEO is that those servers cannot be accessed for maintenance. Consequently, if a satellite or server fails, it remains failed and can only be de-orbited. As with cloud computing, developers expect their applications to be highly available in a LEO edge environment as well. Consequently, a LEO edge platform needs to abstract from the widely distributed and heterogeneous underlying infrastructure to provide fault-tolerance.


Fixed Server Capabilities. Not being able to access individual servers directly also means that they cannot be upgraded. Over the lifetime of a satellite, typically about 5 years, the server capabilities and, with it, the total capability of the constellation of servers, remain fixed.


Fixed Number of Servers. Horizontal scalability is also limited, as we can place servers only on satellites that are part of the constellation and the size of the constellation cannot be changed easily. Launching and deploying additional satellites requires approval by governmental agencies and competing space Internet companies may lobby to limit constellation sizes, especially as LEO is a limited resource



OrbitsEdge Plans Racks in Space

Florida-based OrbitsEdge is embracing a data center in orbit model, taking off-the-shelf rackmount servers and bolting them into a satellite bus (the structural frame housing payloads). “We’re both edge computing and data center,” said Rick Ward, Chief Technical Officer of OrbitsEdge. “We want to put big-performance computing infrastructure into space to process data, cleanse it, aggregate data from multiple sources and analyze it. We are that missing piece of the infrastructure to commercial space.”


OrbitsEdge is able to communicate with other satellites to collect and process their data, as well as performing overhead edge computing where a traditional data center is unavailable or not close enough. The company sees opportunities in offloading and storing data from Earth Observation satellites, processing it into immediately usable imagery, and sending the results directly to end-users in the field. It has had discussions with the U.S. Department of Defense, NASA, and commercial cloud providers on how such non-traditional resources could be useful for various use cases on Earth, in space, and on the surface of other celestial bodies.


“It’s another location for processing data above the clouds,” said Sylvia France, President of OrbitsEdge. “There’s a lot of interest in fintech, being able to make buy/sell decisions based on counting cars in parking lots. We’re also talking to entertainment companies as well, from space tourists to augmented reality firms.”


The OrbitsEdge SatFrame is the company’s proprietary satellite bus, with a standardized 19-inch server rack with available volume for 5U of hardware. The company’s first two SatFrame pathfinder satellites will support 18-inch deep hardware with production designs capable to grow to support full-sized 36 inch deep hardware.


“It’s another location for processing data above the clouds,” said Sylvia France, President of OrbitsEdge. “There’s a lot of interest in fintech, being able to make buy/sell decisions based on counting cars in parking lots. We’re also talking to entertainment companies as well, from space tourists to augmented reality firms.”


The OrbitsEdge SatFrame is the company’s proprietary satellite bus, with a standardized 19-inch server rack with available volume for 5U of hardware. The company’s first two SatFrame pathfinder satellites will support 18-inch deep hardware with production designs capable to grow to support full-sized 36 inch deep hardware.


LEOcloud establishes partnerships for satellite-based cloud computing

Satellite communications startup LEOcloud announced a partnership in July 2021 with supercomputer firm Ramon.Space to develop satellite-based cloud computing.


LEOcloud intends to offer “low latency, highly secure, high availability” cloud services, linking customers on the ground with “satellite data suppliers, hybrid cloud edge computing services and global connectivity” in Phase 1 of its strategy, according to a LEOcloud PowerPoint presentation. In Phase 2, LEOcloud, “will develop, launch and operate a satellite-based cloud infrastructure providing low latency, secure, high availability, mission-critical cloud services,” according to the presentation.


“Having access to data from space assets quickly and reliably is absolutely critical to the success of space missions,” Jonata Puglia, Leaf Space co-founder and CEO, said in a statement. Working with LEOcloud will enhance Leaf Space’s ground segment as a service business, he added.


AWS successfully runs AWS compute and machine learning services on an orbiting satellite

Amazon Web Services (AWS) announced in Nov 2022 that it successfully ran a suite of AWS compute and machine learning (ML) software on an orbiting satellite, in a first-of-its-kind space experiment. The experiment, conducted over the past 10 months in low Earth orbit (LEO), was designed to test a faster, more efficient method for customers to collect and analyze valuable space data directly on their orbiting satellites using the cloud.

Providing AWS edge capabilities onboard an orbiting satellite for the first time lets customers automatically analyze massive volumes of raw satellite data in orbit and only downlink the most useful images for storage and further analysis, driving down cost and enabling timely decision making.

“Using AWS software to perform real-time data analysis onboard an orbiting satellite, and delivering that analysis directly to decision makers via the cloud, is a definite shift in existing approaches to space data management. It also helps push the boundaries of what we believe is possible for satellite operations,” said Max Peterson, AWS vice president, worldwide public sector. “Providing powerful and secure cloud capability in space gives satellite operators the ability to communicate more efficiently with their spacecraft and deliver updated commands using AWS tools they’re familiar with.”

AWS is committed to eliminating technical challenges associated with operating in space, including high latency and limited-bandwidth networks. AWS collaborated with D-Orbit and Unibap, two of its global space partners, to directly address these challenges as they apply to satellite operations.

D-Orbit is a leader in the space logistics and transportation service industry and a member of the AWS Partner Network (APN). By applying AWS compute and machine learning services to Earth Observation (EO) imagery, D-Orbit was able to rapidly analyze large quantities of space data directly onboard its orbiting ION satellite.

“Our customers want to securely process increasingly large amounts of satellite data with very low latency,” said Sergio Mucciarelli, vice president of commercial sales of D-Orbit. “This is something that is limited by using legacy methods, downlinking all data for processing on the ground. We believe in the drive towards edge computing, and that it can only be done with space-based infrastructure that is fit for purpose, giving customers a high degree of confidence that they can run their workloads and operations reliably in the harsh space operating environment.”

The teams collaborated to build a software prototype that would include the tools they together identified as essential for the EO mission, including AWS ML models to analyze satellite imagery in real time, and AWS IoT Greengrass to provide cloud management and analytics even during periods of limited connectivity.

The AWS software prototype was integrated onto a space-qualified processing payload built by Unibap, a high-tech company based in Sweden and another AWS Partner. The Unibap processing payload was then integrated onto a D-Orbit ION satellite and launched into space.

“We want to help customers quickly turn raw satellite data into actionable information that can be used to disseminate alerts in seconds, enable onboard federated learning for autonomous information acquisition, and increase the value of data that is downlinked,” said Dr. Fredrik Bruhn, chief evangelist in digital transformation and co-founder of Unibap. “Providing users real-time access to AWS edge services and capabilities on orbit will allow them to gain more timely insights and optimize how they use their satellite and ground resources.”

Throughout the experiment, the team applied various ML models to satellite sensor data to quickly and automatically identify specific objects both in the sky – such as clouds and wildfire smoke – and objects on Earth including buildings and ships.

Raw satellite images and datasets like these are usually quite large, so the team created a way to break down the large data files into smaller ones. Using AWS AI and ML services helps reduce the size of images by up to 42 percent, increasing processing speeds and enabling real-time inferences on-orbit. The team managed the bidirectional movement of space data over multiple ground station contacts to provide allowance for an increased delay tolerance between communications. This was achieved by managing a reliable TCP/IP proxy between the satellite and the AWS Cloud. This modification made it simpler for ground crews to manage the file transfers automatically, without manually processing the downlinks over multiple contacts.



As adoption of cloud computing continues, NSR’s latest report, Cloud Computing via Satellite, 2nd Edition (CCvS2) forecasts $21 billion cumulative cloud services revenues by 2030 across four key market segments. The impending wave of both LEO, MEO and GEO-HTS satcom services is set to significantly boost long-term cloud adoption and enhance market engagement opportunity, with 233 exabytes of traffic projected.


“The transformation brought about by the adoption of cloud computing is only beginning to impact the satellite sector,” states Shivaprakash Muruganandham, NSR Senior Analyst and report author. “Numerous verticals from cloud-hosted applications to cloud storage/processing by geospatial analytics providers, will see change and development.”


Partnership with large IT and cloud players drives market capture and growth opportunity across multiple segments. Within those core segments, Satellite Communications will continue to lead traffic via satellite, while Earth Observation data downlinks lead in revenues, representing a $10 billion opportunity, as ground station and data relay services enter the downlink market.


“While growth opportunity is forecast for both existing and nascent markets, cost scalability remains a challenge,” added NSR Analyst and Report co-author, Arthur Van Eeckhout. “However, cloud adoption is dramatically decreasing the legacy knowledge requirements for engagement, lowering the barriers to entry for space-derived data services. Today, start-ups born in the cloud have greater opportunities available to them than in the past.”



References and Resources also include:




About Rajesh Uppal

Check Also

China’s Tiangong Space Station: A Beacon of Innovation and Global Collaboration

China’s Tiangong space station, also known as the Heavenly Palace, is a testament to the …

error: Content is protected !!