The Internet of Things (IoT) is offering unprecedented observational data that are used for managing Smart City utilities. Edge and Fog gateway devices are an integral part of IoT deployments to acquire real-time data and enact controls. Recently, Edge-computing is emerging as first-class paradigm to complement Cloud-centric analytics. But a key limitation is the lack of a platform-as-a-service for applications spanning Edge and Cloud. Here, we propose ( ECHO ), an orchestration platform for dataflows across distributed resources. ( ECHO ) ’s hybrid dataflow composition can operate on diverse data models – streams, micro-batches and files, and interface with native runtime engines like TensorFlow and Storm to execute them. It manages the application’s lifecycle, including container-based deployment and a registry for state management. ( ECHO ) can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources. We validate the ( ECHO ) platform for executing video analytics and sensor streams for Smart Traffic and Smart Utility applications on Raspberry Pi, NVidia TX1, ARM64 and Azure Cloud VM resources, and present our results.
@cite_9: The ubiquitous deployment of mobile and sensor devices is creating a new environment, namely the Internet of Things(IoT), that enables a wide range of future Internet applications. In this work, we present Mobile Fog, a high level programming model for the future Internet applications that are geospatially distributed, large-scale, and latency-sensitive. We analyze use cases for the programming model with camera network and connected vehicle applications to show the efficacy of Mobile Fog. We also evaluate application performance through simulation.
@cite_9 has proposed a programming model for composing IoT applications across mobile, Fog and Cloud layers. They consider a multi-way 3-level dataflow model with computation starting in the Cloud, elastic resources acquired in the Cloud and Fog, and communication possible between all 3 layers. Each edge has one Fog parent based on spatial proximity, that may be reassigned. While a useful abstraction, their strictly hierarchical resource and dataflow model are much more restrictive that our use of any network topology and a directed graph as dataflow. Theirs effectively degenerates to a client-server model.