Live Migration of Video Analytics Applications in Edge Computing.

In order to schedule resources efficiently or maintain applications' continuity for mobile customers, edge platforms often need to adaptively migrate the applications on them. However, our measurement shows that existing migration solutions cannot solve the issue of migrating video analytics applications in edge computing because the memory states of video analytics applications have different characteristics from other applications. We conduct a breakdown analysis of the memory states of video analytics applications, and propose to treat three types of states separately with three different techniques, i.e. , warm-up, sync, and replay, to minimize the negative influence of migrations on application performance. Based on this idea, we implement a prototype system in which two new components, i.e. , state store and sidecar , are designed to achieve near-transparent live migration with minimal application code modifications. Evaluation experiments demonstrate that the time of application interruption caused by migrating a video analytics application with our solution is less than 405ms, and our solution does not consume much resources.