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.
翻译：未来AI应用需要现有、 云依赖性系统架构无法提供的性能、 可靠性和隐私。 在本条中, 我们研究在设备- 介质- 球状连续体中的管弦, 并关注资源管弦的边缘 AI 。 我们声称, 为了支持设备- 介质- 球状计算连续体中不断增长的智能应用要求, 资源管弦需要拥抱边缘的AI, 并强调当地自主和智慧。 为了证明这一主张的合理性, 我们为连续管弦划提供了一个总体定义, 并审视当前和新出现的管弦划模式如何适合计算连续体。 我们描述了某些可能影响未来管弦划的主要新兴研究主题, 并为包含这些研究主题的管弦划模式提供了早期愿景。 最后, 我们调查当前关键的边缘AI 方法, 并研究它们如何帮助实现未来连续管弦的愿景 。
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