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ECHO: An adaptive orchestration platform for hybrid dataflows across cloud and edge

Ravindra, Pushkara and Khochare, Aakash and Reddy, Siva Prakash and Sharma, Sarthak and Varshney, Prateeksha and Simmhan, Yogesh (2017) ECHO: An adaptive orchestration platform for hybrid dataflows across cloud and edge. In: 15th International Conference on Service-Oriented Computing, ICSOC 2017, 13 November 2017 through 16 November 2017, Malaga, pp. 395-410.

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Official URL: https://doi.org/10.1007/978-3-319-69035-3_28


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, FogandCloud 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.

Item Type: Conference Paper
Publisher: Springer Verlag
Additional Information: The Copyright of the article belongs to the Springer.
Keywords: Data flow analysis; Distributed computer systems; Gateways (computer networks); Mobile telecommunication systems; Platform as a Service (PaaS); Smart city; Distributed resources; Edge computing; Gateway devices; Internet of thing (IOT); Observational data; Real-time data; State management; Video analytics; Internet of things
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 07 Jun 2022 05:02
Last Modified: 08 Jun 2022 05:55
URI: https://eprints.iisc.ac.in/id/eprint/72608

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