# ORCHIDE: Using EOS for space edge computing

- Institution: CERN
- Speaker: Sergiu Weisz
- Year: 2026
- Language: English
- Subjects: Computer science, Astronomy and space
- Source: [CERN](https://videos.cern.ch/record/3023547)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Modern Low Earth Orbit satellites increasingly support on-board data processing using GPUs and FPGAs, enabling multi-purpose use cases beyond simple data capture. However, orchestrating computational workloads across single or multi-node satellite systems requires an efficient shared storage solutions that can handle the constraints of space environments.

This contribution presents the integration of EOS into the ORCHIDE project - an orchestrator platform for managing multi-tenant satellite workloads. ORCHIDE enables service operators to submit processing jobs that execute on sensor data stored locally, eliminating the need to download raw data to ground stations. The platform's requirement for platform-agnostic deployment across heterogeneous satellite architectures necessitated a shared filesystem with low overhead, fault tolerance, and long-term reliability.

We document the comparative analysis that led to selecting EOS over alternative solutions, present performance benchmarks demonstrating different workloads using shared filesystems and describe the integration enabling ORCHIDE to manage storage across distributed satellite compute nodes.

00:00:00 Slide 1

00:00:26 Slide 2

00:01:26 Slide 3

00:02:21 Slide 4

00:03:27 Slide 5

00:04:07 Slide 6

00:05:02 Slide 7

00:06:28 Slide 8

00:07:51 Slide 9

00:09:04 Slide 10

00:10:28 Slide 11

00:11:59 Slide 12

00:13:32 Slide 13

00:14:33 Slide 14

00:16:36 Slide 15

00:17:09 Slide 16

00:17:28 Slide 17

00:18:28 Slide 18

00:19:28 Slide 19

00:20:25 Slide 20

00:22:04 Slide 21

00:23:07 Slide 22

00:23:55 Slide 23

## Lectures

1. [ORCHIDE: Using EOS for space edge computing](https://invisiblenetwork.ai/learn/cern/3023547/61683e67620a), 29 min (CERN's player)