[Back]

H. Kokkonen, L. Loven, N. Hossein Motlagh, J. Partala, A. Gonzalez-Gil, E. Sola, I. Angulo, M. Liyanage, T. Leppänen, T. Nguyen, P. Kostakos, M. Bennis, S. Tarkoma, S. Dustdar, S. Pirttikangas, J. Riekki:
"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration";
Report for CoRR - Computing Research Repository; Report No. arXiv:2205.01423, 2022; 53 pages.

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 AI for edge, that is, the AI methods used in 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.

edge AI, edge intelligence, computing continuum, orchestration