Architecture for Orchestrating Dynamic DNN-Powered Image Processing Tasks in Edge and Cloud Devices

Abstract

DNN processing on image streams has opened the possibility for new and innovative applications. Some of those would benefit from performing the computation locally, avoiding incurring into latencies due to data travelling to image processing services in the cloud, and thus allowing for faster response times. New devices like the GPU-accelerated NVIDIA Jetson family, such as the Jetson Nano, are capable of running modern DNN image processing models, offering an affordable, powerful and scalable local alternative to cloud processing. Performing local image processing can also benefit security and even GDPR compliance, potentially easing the deployment of this solutions. Not only that, but local image processing can also bring the possibility of applying these techniques in areas with reduced connectivity, where cloud-based solutions are unfeasible . In this work, we propose an architecture for the orchestration of DNN accelerated image processing on IoT devices, based on FogFlow; an orchestration platform capable of leveraging cloud and edge resources. FogFlow is part of the FIWARE initiative and is based in the NGSI family of standards, widely applied in Smart City, Smart Building and Smart Home solutions, making it an easy-to-integrate technology.INDEX TERMS DNN image processing, IoT, orchestration.

Dynamic, Context-Aware Cross-Layer Orchestration of Containerized Applications

“…The load on system resources as well as the number of users and the data being consumed change constantly, and both users and system nodes may be mobile thus adding to the dynamics of the overall system [33], [34]. Therefore, integrating intelligence into this process is both challenging and essential [35]. Managing computing resources and optimizing costs on multi-cluster Edge-Cloud environments are highly demanding tasks, as container adoption is growing using multiple container management platforms [25].…”