OrbiFaaS: An orbital method to build Continuum Earth Observation Systems Articles uri icon

publication date

  • July 2025

start page

  • 1

end page

  • 14

volume

  • 174

International Standard Serial Number (ISSN)

  • 0167-739X

Electronic International Standard Serial Number (EISSN)

  • 1872-7115

abstract

  • Earth observation (EO) problems require processing large volumes of data, such as those involved in environmental studies using diverse spatiotemporal variables. In this context, the computing continuum offers a promising model to support EO tasks. It enables low-latency data processing near ground stations and broad data sharing through the cloud.
    Nevertheless, designing systems in the computing continuum introduces challenges. One is the coordination of distributed entities across different computational environments (e.g., the edge, the fog, or the cloud). Another is managing data across heterogeneous infrastructures.
    This paper presents OrbiFaaS, a serverless-based method for building Continuum Earth Observation Systems (EOS) using an orbital model. OrbiFaaS organizes a continuum EOS as a system of multiple orbits, each representing a different computational environment arranged around a core of data sources.
    Orbits near the core correspond to low-latency environments (e.g., the edge), while those farther away exhibit higher latencies (e.g., the cloud). Each orbit contains satellites, which represent service provider infrastructures. Organizations can use these satellites to deploy multiple data management services, such as containerized microservices or functions. Satellites are interconnected using data containers that establish communication channels based on available filesystem, memory, and network resources.
    We evaluate OrbiFaaS in a case study involving the processing of satellite images across multiple environments. Our experimental results demonstrate the feasibility and efficiency of OrbiFaaS for constructing Earth observation systems.

subjects

  • Computer Science

keywords

  • earth observation services; serverless computing; e-science; big data; environmental indexes