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Application caching for cloud-sensor systems

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MSWiM '14: Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

Pages 303 - 306

https://doi.org/10.1145/2641798.2641814

Published: 21 September 2014 Publication History

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Abstract

Driven by critical and pressing smart city applications, accessing massive numbers of sensors by cloud-hosted services is becoming an emerging and inevitable situation. Naïvely connecting massive numbers of sensors to the cloud raises major scalability and energy challenges. An architecture embodying distributed optimization is needed to manage the scale and to allow limited energy sensors to last longer in such a dynamic and high-velocity big data system. We developed a multi-tier architecture which we call Cloud, Edge and Beneath (CEB). Based on CEB, we propose an Application Fragment Caching Algorithm (AFCA) which selectively caches application fragments from the cloud to lower layers of CEB to improve cloud scalability. Through experiments, we show and measure the effect of AFCA on cloud scalability.

References

[1]

Xu, Y. and Helal, A. 2014. Scalable Cloud-Sensor Architecture for the Internet of Things, Submitted for pub. UF Mobile and Pervasive Computing Lab Technical Report: http://www.icta.ufl.edu/projects/publications/ceb.pdf

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[2]

King, J., Bose, R., Yang, H., Pickles, S., and Helal, A. 2006. Atlas - A Service-Oriented Sensor Platform: Hardware and Middleware to Enable Programmable Pervasive Spaces. In Proceedings of the first IEEE International Workshop on Practical Issues in Building Sensor Network Applications. Tampa, Florida, November 2006, 630--638.

Google Scholar

[3]

Deugd, S., Carroll, R., Kelly, K.E., Millett, B., and Ricker. J. 2006. SODA: Service Oriented Device Architecture. IEEE Pervasive Computing, Volume 5 Issue 3, July 2006.

Digital Library

Google Scholar

[4]

OSGi 4.2. http://www.osgi.org/Download/Release4V42

Google Scholar

[5]

OSGi Cloud Computing. https://www.osgi.org/bugzilla/show_bug.cgi?id=114.

Google Scholar

[6]

Amazon EC2. http://aws.amazon.com

Google Scholar

[7]

Xu, Y. and Helal, A. 2014. An Optimization Framework for Cloud-Sensor Systems. To be submitted. http://www.icta.ufl.edu/projects/publications/waterfall.pdf

Google Scholar

Cited By

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Guan SBoukerche ALoureiro A(2021)Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive CloudletsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.29808476:2(334-346)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TSUSC.2020.2980847
Vambe WChang CSibanda K(2020)A Review of Quality of Service in Fog Computing for the Internet of ThingsInternational Journal of Fog Computing10.4018/IJFC.20200101023:1(22-40)Online publication date: Jan-2020
https://doi.org/10.4018/IJFC.2020010102
Zhang LLi CWang PLiu YHu YChen QGuo M(2019)Characterizing and orchestrating NFV-ready servers for efficient edge data processingProceedings of the International Symposium on Quality of Service10.1145/3326285.3329057(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3326285.3329057
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Index Terms

Application caching for cloud-sensor systems
1. Networks
  1. Network protocols

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Published In

MSWiM '14: Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

September 2014

352 pages

ISBN:9781450330305

DOI:10.1145/2641798

General Chairs:
Ravi Prakash
University of Texas at Dallas, USA
,
Azzedine Boukerche
University of Ottawa, Canada
,
Program Chairs:
Cheng Li
Memorial University of Newfoundland, Canada
,
Falko Dressler
University of Paderborn, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 September 2014

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MSWiM'14: 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

September 21 - 26, 2014

QC, Montreal, Canada

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MSWiM '14 Paper Acceptance Rate 32 of 128 submissions, 25%;

Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Guan SBoukerche ALoureiro A(2021)Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive CloudletsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.29808476:2(334-346)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TSUSC.2020.2980847
Vambe WChang CSibanda K(2020)A Review of Quality of Service in Fog Computing for the Internet of ThingsInternational Journal of Fog Computing10.4018/IJFC.20200101023:1(22-40)Online publication date: Jan-2020
https://doi.org/10.4018/IJFC.2020010102
Zhang LLi CWang PLiu YHu YChen QGuo M(2019)Characterizing and orchestrating NFV-ready servers for efficient edge data processingProceedings of the International Symposium on Quality of Service10.1145/3326285.3329057(1-10)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1145/3326285.3329057
Li CXue YWang JZhang WLi T(2018)Edge-Oriented Computing ParadigmsACM Computing Surveys10.1145/315481551:2(1-34)Online publication date: 17-Apr-2018
https://dl.acm.org/doi/10.1145/3154815
Xu YHelal A(2016)Scalable Cloud–Sensor Architecture for the Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2015.24555553:3(285-298)Online publication date: Jun-2016
https://doi.org/10.1109/JIOT.2015.2455555
Zhuang ZRamachandra HSridharan BDuncan BGopalakrishna KIm J(2016)SmartCache: Application layer caching to improve performance of large-scale memory mapping2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7840762(1542-1550)Online publication date: Dec-2016
https://doi.org/10.1109/BigData.2016.7840762
Xu YHelal S(2014)An Optimization Framework for Cloud-Sensor SystemsProceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science10.1109/CloudCom.2014.52(38-45)Online publication date: 15-Dec-2014
https://dl.acm.org/doi/10.1109/CloudCom.2014.52

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Cloud service engineering

Developing an Elastic Cloud Computing Application through Multi-Agent Systems

DDQN-based online computation offloading and application caching for dynamic edge computing service management

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