More Web Proxy on the site http://driver.im/

tutorial

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Authors:

Anne-Cécile Orgerie,

Manish Parashar,

Jean-Marc MenaudAuthors Info & Claims

CCGrid '17: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

Pages 186 - 195

https://doi.org/10.1109/CCGRID.2017.92

Published: 14 May 2017 Publication History

Abstract

The emergence of Internet of Things (IoT) is participating to the increase of data- and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

References

[1]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications," IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347--2376, 2015.

Digital Library

[2]

S. C. B. Intelligence, "Six Technologies with Potential Impacts on US Interests out to 2025," National Intelligent Concil, Tech. Rep., 2008.

[3]

S. Abdelwahab, B. Hamdaoui, M. Guizani, and A. Rayes, "Enabling Smart Cloud Services Through Remote Sensing: An Internet of Everything Enabler," IEEE Internet of Things Journal, vol. 1, no. 3, pp. 276--288, June 2014.

[4]

L. M. Vaquero and L. Rodero-Merino, "Finding Your Way in the Fog: Towards a Comprehensive Definition of Fog Computing," ACM SIGCOMM Computer Communication Review, vol. 44, no. 5, pp. 27--32, Oct. 2014.

Digital Library

[5]

W. Hu, Y. Gao, K. Ha, J. Wang, B. Amos, Z. Chen, P. Pillai, and M. Satyanarayanan, "Quantifying the Impact of Edge Computing on Mobile Applications," in ACM SIGOPS Asia-Pacific Workshop on Systems, 2016, pp. 5:1--5:8.

Digital Library

[6]

B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick, and D. S. Nikolopoulos, "Challenges and Opportunities in Edge Computing," CoRR, vol. abs/1609.01967, 2016.

[7]

T. Bawden, "Global warming: Data centres to consume three times as much energy in next decade, experts warn," Independent, http://www.independent.co.uk/environment/global-warming-data-centres- to-consume-three-times-as-much-energy-in-next-decade-experts-warn- a6830086.html, 2016.

[8]

Í. Goiri, W. Katsak, K. Le, T. D. Nguyen, and R. Bianchini, "Parasol and greenswitch: managing datacenters powered by renewable energy," in ACM SIGARCH Computer Architecture News, vol. 41, no. 1. ACM, 2013, pp. 51--64.

Digital Library

[9]

F. Yang, S. Wang, J. Li, Z. Liu, and Q. Sun, "An overview of internet of vehicles," China Communications, vol. 11, no. 10, pp. 1--15, 2014.

[10]

A. Ishii and T. Suzumura, "Elastic stream computing with clouds," in Cloud Computing (CLOUD), 2011 IEEE International Conference on. IEEE, 2011, pp. 195--202.

Digital Library

[11]

E. Baccarelli, N. Cordeschi, A. Mei, M. Panella, M. Shojafar, and J. Stefa, "Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study," IEEE Network, vol. 30, no. 2, pp. 54--61, 2016.

Digital Library

[12]

W. Zhu, C. Luo, J. Wang, and S. Li, "Multimedia cloud computing," IEEE Signal Processing Magazine, vol. 28, no. 3, pp. 59--69, 2011.

[13]

Y. Li, A.-C. Orgerie, and J.-M. Menaud, "Opportunistic scheduling in clouds partially powered by green energy," in GreenCom: IEEE International Conference on Green Computing and Communications, 2015, pp. 448--455.

Digital Library

[14]

Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg, "Toward a realistic performance analysis of storage systems in smart grids," IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 402--410, 2015.

[15]

P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1. IEEE, 2001.

[16]

R. E. Schapire and Y. Singer, "Improved boosting algorithms using confidence-rated predictions," Machine learning, vol. 37, no. 3, pp. 297--336, 1999.

Digital Library

[17]

J. Diaz-Montes, M. Zou, I. Rodero, and M. Parashar, "Enabling autonomic computing on federated advanced cyberinfrastructures," in Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference. ACM, 2013, p. 20.

Digital Library

[18]

P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, and M. Satyanarayanan, "Scalable crowd-sourcing of video from mobile devices," in ACM International conference on Mobile systems, applications, and services, 2013, pp. 139--152.

Digital Library

[19]

A. Anjum, T. Abdullah, M. Tariq, Y. Baltaci, and N. Antonopoulos, "Video stream analysis in clouds: An object detection and classification framework for high performance video analytics," IEEE Transactions on Cloud Computing, 2016.

[20]

Y. Mao, J. Zhang, and K. B. Letaief, "Dynamic computation offloading for mobile-edge computing with energy harvesting devices," arXiv preprint arXiv:1605.05488, 2016.

Digital Library

[21]

N. Beldiceanu, B. Dumas Feris, P. Gravey, S. Hasan, C. Jard, T. Ledoux, Y. Li, D. Lime, G. Madi-Wamba, J.-M. Menaud, P. Morel, M. Morvan, M.-L. Moulinard, A.-C. Orgerie, J.-L. Pazat, O. H. Roux, and A. Sharaiha, "Towards energy-proportional Clouds partially powered by renewable energy," Computing, p. 20, 2016.

Digital Library

[22]

H. Chen, T. N. Cong, W. Yang, C. Tan, Y. Li, and Y. Ding, "Progress in electrical energy storage system: A critical review," Progress in Natural Science, vol. 19, no. 3, pp. 291--312, 2009.

[23]

K. Divya and J. Østergaard, "Battery energy storage technology for power systems?an overview," Electric Power Systems Research, vol. 79, no. 4, pp. 511--520, 2009.

[24]

D. Balouek et al., "Adding virtualization capabilities to the Grid'5000 testbed," in Cloud Computing and Services Science. Springer, 2013, vol. 367, pp. 3--20.

[25]

F. Jalali, K. Hinton, R. Ayre, T. Alpcan, and R. S. Tucker, "Fog Computing May Help to Save Energy in Cloud Computing," IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1728--1739, 2016.

Digital Library

[26]

A.-C. Orgerie, M. Dias de Assunção, and L. Lefèvre, "A survey on techniques for improving the energy efficiency of large-scale distributed systems," ACM Computing Surveys (CSUR), vol. 46, no. 4, p. 47, 2014.

Digital Library

[27]

I. Cuadrado-Cordero, F. Cuadrado, C. Phillips, A.-C. Orgerie, and C. Morin, "Microcities: a Platform based on Microclouds for Neighborhood Services," Inria, Research Report RR-8885, 2016.

[28]

"https://www.ffmpeg.org," https://www.ffmpeg.org.

Cited By

Santos GTakako Endo PFerreira Da Silva Lisboa Tigre MFerreira Da Silva LSadok DKelner JLynn T(2018)Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructuresJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-018-0118-37:1(1-22)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1186/s13677-018-0118-3

Recommendations

Towards energy-proportional clouds partially powered by renewable energy

With the emergence of the Future Internet and the dawning of new IT models such as cloud computing, the usage of data centers (DC), and consequently their power consumption, increase dramatically. Besides the ecological impact, the energy consumption is ...
Renewable Energy-Aware Heuristic Algorithms for Edge Server Selection for Stream Data Processing
Computational Science – ICCS 2021
Abstract
Internet of Things and Edge computing are evolving, bringing data processing closer to the source and a result closer to the network’s edge. This distributed processing can increase energy consumption and carbon footprint. One solution to overcome ...
Decentralized Edge Clouds

Cloud computing services are traditionally deployed on centralized computing infrastructures confined to a few data centers, while cloud applications run in a single data center. However, the cloud's centralized nature can be limiting in terms of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CCGrid '17: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

May 2017

1167 pages

ISBN:9781509066100

Sponsors

Publisher

IEEE Press

Publication History

Published: 14 May 2017

Check for updates

Qualifiers

Tutorial
Research
Refereed limited

Conference

CCGrid '17

Sponsor:

SIGARCH

CCGrid '17: 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

May 14 - 17, 2017

Madrid, Spain

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
116
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Santos GTakako Endo PFerreira Da Silva Lisboa Tigre MFerreira Da Silva LSadok DKelner JLynn T(2018)Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructuresJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-018-0118-37:1(1-22)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1186/s13677-018-0118-3

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents