Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

TL;DR

This paper explores the challenges and future directions of resource orchestration in the device-edge-cloud continuum, emphasizing the role of edge AI for local autonomy and improved performance in intelligent applications.

Contribution

It provides a comprehensive definition of continuum orchestration, analyzes current paradigms, identifies emerging research themes, and surveys key edge AI methods to support future orchestration paradigms.

Findings

Current orchestration paradigms are insufficient for the computing continuum

Emerging research themes include local autonomy and intelligence at the edge

Edge AI methods can significantly enhance resource orchestration

Abstract

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.