Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
This addresses the problem of insufficient system architectures for AI applications in the computing continuum, but it is incremental as it builds on existing orchestration paradigms and edge AI methods.
The paper tackles the challenge of meeting performance, reliability, and privacy requirements for future AI applications by proposing that resource orchestration in the device-edge-cloud continuum must incorporate edge AI with local autonomy and intelligence, and it surveys current methods to support this vision.
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.