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.
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- 球状连续体中进行交响, 并关注在边缘的 AI 方法, 即用于资源管弦的 AI 方法。 我们声称, 为了支持在设备- 介质- 球状计算连续体中不断增长的智能应用要求, 资源管弦化需要拥抱边缘的AI, 并强调本地自主和智慧。 为了证明这一主张合理, 我们为连续管弦化提供了一个总体定义, 并审视当前和新兴的管弦化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化的主要研究主题, 并为包含这些研究主题的管弦化模式提供早期愿景。 最后, 我们调查当前的关键边缘的AI 方法, 并研究它们如何帮助实现未来连续管弦化的愿景 。
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