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A Reference Architecture for Cloud-Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted

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This summary is machine-generated.

A new meta-operating system (RAMOS) architecture enables a trusted cloud-to-edge-to-IoT continuum for data-intensive applications. This system supports dynamic, distributed intelligence and privacy preservation at the edge.

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IoT–edge–cloud continuumdecentralized intelligenceedge computingfederated learningmeta-operating systemreference architectureswarm learning

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Edge Computing

Background:

  • Current hierarchical architectures struggle with the data explosion from IoT proliferation.
  • Intelligent applications require seamless coordination across cloud, edge, and IoT devices.
  • Existing solutions offer only partial support for the cloud-to-edge-to-IoT continuum.

Purpose of the Study:

  • To present a reference architecture for a meta-operating system (RAMOS).
  • To enable a dynamic, distributed, and trusted continuum for next-generation edge applications.
  • To facilitate secure data placement at the origin and support heterogeneous environments.

Main Methods:

  • Development of a layered reference architecture for RAMOS.
  • Detailed description of RAMOS building blocks and their interconnections.
  • Illustration of RAMOS in five practical application scenarios.

Main Results:

  • RAMOS architecture supports domain-agnostic and heterogeneous device integration.
  • The system ensures secure and trusted data placement at the data source.
  • Demonstrated applicability in industrial and societal scenarios focusing on distributed intelligence and privacy.

Conclusions:

  • RAMOS provides a robust framework for advanced edge computing applications.
  • The architecture promotes distributed intelligence, privacy preservation, and environmental footprint minimization.
  • RAMOS has significant business potential within an open edge ecosystem and contributes to climate net neutrality.