Predictive GPU-based ADAS management in energy-conscious smart cities

Pérez Morillo, Sergio and Pérez Sánchez, Jaime and Arroba García, Patricia and Blanco Andrés, Roberto and Ayala Rodrigo, José Luis and Moya Fernández, José Manuel (2020). Predictive GPU-based ADAS management in energy-conscious smart cities. In: "IEEE International Smart Cities Conference (ISC2) 2019", 14/10/2019 - 17/10/2019, Casablanca, Marruecos. ISBN 978-1-7281-0847-6. pp. 454-459. https://doi.org/10.1109/ISC246665.2019.9071685.

Description

Title: Predictive GPU-based ADAS management in energy-conscious smart cities
Author/s:
  • Pérez Morillo, Sergio
  • Pérez Sánchez, Jaime
  • Arroba García, Patricia
  • Blanco Andrés, Roberto
  • Ayala Rodrigo, José Luis
  • Moya Fernández, José Manuel
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE International Smart Cities Conference (ISC2) 2019
Event Dates: 14/10/2019 - 17/10/2019
Event Location: Casablanca, Marruecos
Title of Book: Proceedings of IEEE International Smart Cities Conference (ISC2) 2019
Date: April 2020
ISBN: 978-1-7281-0847-6
Subjects:
Freetext Keywords: Predictive Power Modeling; Edge Computing; Artificial Neural Network; Driving Assistance
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The demand of novel IoT and smart city applications is increasing significantly and it is expected that by 2020 the number of connected devices will reach 20.41 billion. Many of these applications and services manage real-time data analytics with high volumes of data, thus requiring an efficient computing infrastructure. Edge computing helps to enable this scenario improving service latency and reducing network saturation. This computing paradigm consists on the deployment of numerous smaller data centers located near the data sources. The energy efficiency is a key challenge to implement this scenario, and the management of federated edge data centers would benefit from the use of microgrid energy sources parameterized by user's demands. In this research we propose an ANN predictive power model for GPU-based federated edge data centers based on data traffic demanded by the application. We validate our approach, using real traffic for a state-of-the-art driving assistance application, obtaining 1 hour ahead power predictions with a normalized root-mean-square deviation below 7.4% when compared with real measurements. Our research would help to optimize both resource management and sizing of edge federations.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainIDI-20171194UnspecifiedUnspecifiedRefrigeración de servidores de centros de datos de alta densidad por inmersión en fluido refrigerante bi-fase
Government of SpainRTC-2017-6090-3GRID-EUnspecifiedSistema integral para la gestión óptima coordinada de recursos en centros de datos de altas prestaciones
Government of SpainIDI-20171183UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2015-65277-RCOPHERNICOUnspecifiedEfficient heterogeneous computing: from the processor to the datacenter

More information

Item ID: 65205
DC Identifier: https://oa.upm.es/65205/
OAI Identifier: oai:oa.upm.es:65205
DOI: 10.1109/ISC246665.2019.9071685
Official URL: https://ieeexplore.ieee.org/document/9071685
Deposited by: Memoria Investigacion
Deposited on: 19 Apr 2021 13:51
Last Modified: 19 Apr 2021 13:51
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