Abstract
In recent years, more and more developers have been investigating robotic vehicles. Generally, the operations of robotic vehicles rely on navigation assistance systems, which make recommendations to guide robotic vehicles step-by-step using low-cost cameras until reaching the destinations. Some developers have gradually transferred relevant computing tasks of robotic vehicles without powerful computing power and training models to the edge computing platforms. However, none of such existing edge computing based navigation assistance systems can immediately scale a sufficient number of instances to adapt to the changing requested loads of robotic vehicles. In this paper, we propose a Kubernetes edge-powered vision-based navigation assistance system with a novel auto-scaling algorithm, allowing robotic vehicles to request navigation-related services. Once the requested load does not match the current load, the number of instances can be auto-scaled on demand. In order to evaluate the performance of our auto-scaling algorithm, we compare it with two selected auto-scaling algorithms. The experiment results demonstrate that our algorithm can immediately scale up to the most appropriate number of instances to reduce the latency of requests.