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Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing

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Media Query Processing for the Internet-of-Things : Coupling of Device Energy Consumption and Cloud Infrastructure Billing. / Renna, Francesco; Doyle, Joseph; Giotsas, Vasileios; Andreopoulos, Yiannis.

In: IEEE Transactions on Multimedia, Vol. 18, No. 12, 7544517, 01.12.2016, p. 2537-2552.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Renna, F, Doyle, J, Giotsas, V & Andreopoulos, Y 2016, 'Media Query Processing for the Internet-of-Things: Coupling of Device Energy Consumption and Cloud Infrastructure Billing', IEEE Transactions on Multimedia, vol. 18, no. 12, 7544517, pp. 2537-2552. https://doi.org/10.1109/TMM.2016.2600438

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Author

Renna, Francesco ; Doyle, Joseph ; Giotsas, Vasileios ; Andreopoulos, Yiannis. / Media Query Processing for the Internet-of-Things : Coupling of Device Energy Consumption and Cloud Infrastructure Billing. In: IEEE Transactions on Multimedia. 2016 ; Vol. 18, No. 12. pp. 2537-2552.

Bibtex

@article{5eeccfb9e45c4ca08a45bddbb330ca54,
title = "Media 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 Internet-of-Things-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: 1) controlling the device energy consumption when using the service, and 2) 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, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: 1) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service, and 2) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.",
keywords = "Analytic modeling, cloud computing, Internet-of-Things, visual search",
author = "Francesco Renna and Joseph Doyle and Vasileios Giotsas and Yiannis Andreopoulos",
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.",
year = "2016",
month = dec,
day = "1",
doi = "10.1109/TMM.2016.2600438",
language = "English",
volume = "18",
pages = "2537--2552",
journal = "IEEE Transactions on Multimedia",
issn = "1520-9210",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

RIS

TY - JOUR

T1 - Media Query Processing for the Internet-of-Things

T2 - Coupling of Device Energy Consumption and Cloud Infrastructure Billing

AU - Renna, Francesco

AU - Doyle, Joseph

AU - Giotsas, Vasileios

AU - Andreopoulos, Yiannis

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 - 2016/12/1

Y1 - 2016/12/1

N2 - Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things-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: 1) controlling the device energy consumption when using the service, and 2) 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, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: 1) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service, and 2) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) 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 Internet-of-Things-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: 1) controlling the device energy consumption when using the service, and 2) 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, the query volume constraint of each cluster of devices, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: 1) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service, and 2) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.

KW - Analytic modeling

KW - cloud computing

KW - Internet-of-Things

KW - visual search

U2 - 10.1109/TMM.2016.2600438

DO - 10.1109/TMM.2016.2600438

M3 - Journal article

AN - SCOPUS:85000450790

VL - 18

SP - 2537

EP - 2552

JO - IEEE Transactions on Multimedia

JF - IEEE Transactions on Multimedia

SN - 1520-9210

IS - 12

M1 - 7544517

ER -