In the last few years Internet of Things (ioT) applications are moving from the cloud-sensor paradigm to a more variegated structure where IoT nodes Interact with an Intermediate fog computing layer. To enable compute-intensive tasks to be executed near the source of the data, fog computing nodes should provide enough performance and be sufficiently energy efficient to run on the field. Within this context, embedded Field Programmable Gate Array (FPGA) can be used to Improve the performance per Watt ratio of fog computing nodes. In this paper we present Fog Acceleration through Reconigurable Devices (FARD), a distributed system that exploits FPGAs to accelerate compute-intensive tasks In fog computing applications. FARD Is able to efficiently run distributed fog applications thanks to a well-defined application structure, a per-application Isolated network overlay and thanks to the acceleration of tasks. Results show energy efficiency Improvements while efficiently enabling cooperation across fog nodes.

FARD: Accelerating Distributed Fog Computing Workloads through Embedded FPGAs

Barbieri S.;Casasopra F.;Brondolin R.;Santambrogio M. D.
2020-01-01

Abstract

In the last few years Internet of Things (ioT) applications are moving from the cloud-sensor paradigm to a more variegated structure where IoT nodes Interact with an Intermediate fog computing layer. To enable compute-intensive tasks to be executed near the source of the data, fog computing nodes should provide enough performance and be sufficiently energy efficient to run on the field. Within this context, embedded Field Programmable Gate Array (FPGA) can be used to Improve the performance per Watt ratio of fog computing nodes. In this paper we present Fog Acceleration through Reconigurable Devices (FARD), a distributed system that exploits FPGAs to accelerate compute-intensive tasks In fog computing applications. FARD Is able to efficiently run distributed fog applications thanks to a well-defined application structure, a per-application Isolated network overlay and thanks to the acceleration of tasks. Results show energy efficiency Improvements while efficiently enabling cooperation across fog nodes.
fog acceleration
fog computing
FPGA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1146582
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