$$\mathbb { E C H O}$$: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge

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 \(\mathbb {ECHO}\), an orchestration platform for dataflows across distributed resources. \(\mathbb {ECHO}\) ’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. \(\mathbb {ECHO}\) can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources. We validate the \(\mathbb {ECHO}\) 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.