Microservice-based Edge Device Architecture for Video Analytics

With today's ubiquitous deployment of video cameras and other edge devices, progress in edge computing is happening at an incredible speed. Yet, one aspect of real-time video analytics at the edge that is still underdeveloped is the support for processing multitenant, multi-application scenarios with a limited set of resources. Existing systems either fail to provide the necessary performance, or rely too heavily on edge or cloud servers to handle the workload. This work proposes a new approach, inspired by both Function-as-a-Service and microservices architecture in order to efficiently place and execute video analytics pipelines on edge devices. The main contributions of this work are the ability to dynamically add and run new applications on already deployed systems, and the capability to horizontally distribute pipelines across other neigh-bouring edge devices. We prototype an implementation that we evaluate using multiple concurrent applications per device. Results show that our system provides more flexibility for on-the-fly re-configuration than existing works do, with 20 % improvement in latency and 3.9 X increase in throughput.