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  • Query_processing_IoTDI-2016

    Rights statement: ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing. / Renna, Francesco; Doyle, Joseph; Andreopoulos, Yiannis et al.
Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on. Institute of Electrical and Electronics Engineers Inc., 2016. p. 83-94 7471353.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Renna, F, Doyle, J, Andreopoulos, Y & Giotsas, V 2016, Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing. in Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on., 7471353, Institute of Electrical and Electronics Engineers Inc., pp. 83-94, 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016, Berlin, Germany, 4/04/16. https://doi.org/10.1109/IoTDI.2015.37

APA

Renna, F., Doyle, J., Andreopoulos, Y., & Giotsas, V. (2016). Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing. In Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on (pp. 83-94). Article 7471353 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IoTDI.2015.37

Vancouver

Renna F, Doyle J, Andreopoulos Y, Giotsas V. Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing. In Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on. Institute of Electrical and Electronics Engineers Inc. 2016. p. 83-94. 7471353 doi: 10.1109/IoTDI.2015.37

Author

Renna, Francesco ; Doyle, Joseph ; Andreopoulos, Yiannis et al. / Query processing for the internet-of-things : Coupling of device energy consumption and cloud infrastructure billing. Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 83-94

Bibtex

@inproceedings{ed6baa5c87464e8d9e87db160ae85e7d,
title = "Query processing for the internet-of-things: Coupling of device energy consumption and cloud infrastructure billing",
abstract = "Audio/visual recognition and retrieval applications have recently garnered significant attention within Internetof-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: (i) controlling the device energy consumption when using the service; (ii) reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: (i) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) spot instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.",
author = "Francesco Renna and Joseph Doyle and Yiannis Andreopoulos and Vasileios Giotsas",
note = "{\textcopyright}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016 ; Conference date: 04-04-2016 Through 08-04-2016",
year = "2016",
month = may,
day = "17",
doi = "10.1109/IoTDI.2015.37",
language = "English",
pages = "83--94",
booktitle = "Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Query processing for the internet-of-things

T2 - 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016

AU - Renna, Francesco

AU - Doyle, Joseph

AU - Andreopoulos, Yiannis

AU - Giotsas, Vasileios

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/5/17

Y1 - 2016/5/17

N2 - Audio/visual recognition and retrieval applications have recently garnered significant attention within Internetof-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: (i) controlling the device energy consumption when using the service; (ii) reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: (i) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) spot instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.

AB - Audio/visual recognition and retrieval applications have recently garnered significant attention within Internetof-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: (i) controlling the device energy consumption when using the service; (ii) reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: (i) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) spot instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.

U2 - 10.1109/IoTDI.2015.37

DO - 10.1109/IoTDI.2015.37

M3 - Conference contribution/Paper

AN - SCOPUS:84977632392

SP - 83

EP - 94

BT - Internet-of-Things Design and Implementation (IoTDI), 2016 IEEE First International Conference on

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 4 April 2016 through 8 April 2016

ER -