Distributed Edge AI Systems

Edge computing has become a promising computing paradigm for building IoT (Internet of Things) applications, particularly applications with latency and privacy constraints. However, these applications typically tend to be compute-intensive and compute resources are limited at the edge when compared to the cloud, so it is important to efficiently utilize all computing resources available at the edge. A key challenge in utilizing these resources is the scheduling of different computing tasks in a dynamically varying, highly hybrid computing environment. We describe the design, implementation, and evaluation of a dynamic distributed scheduler for the edge that constantly monitors the current state of the computing infrastructure and dynamically schedules various computing tasks to ensure that all application constraints are met in another paper. Based on that, this paper mainly proposes a profile evaluation method and results when applying an augmented reality application on distributed systems at the edge. With that work done, we propose and implement a good solution to efficiently distribute edge AI applications at the edge.