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review-article

ROUTER: : Fog enabled cloud based intelligent resource management approach for smart home IoT devices

Authors:

Sukhpal Singh Gill,

Peter Garraghan,

Rajkumar BuyyaAuthors Info & Claims

Volume 154, Issue C

Pages 125 - 138

https://doi.org/10.1016/j.jss.2019.04.058

Published: 01 August 2019 Publication History

Highlights

•

Designed a request handler mechanism to manage job requests of IoT devices.

•

Proposed PSO based resource scheduling technique for fog-assisted cloud environment.

•

Validated with the help of a case study of IoT based smart home automation.

•

Optimized QoS parameters such as response time, bandwidth, energy and latency.

Abstract

There is a growing requirement for Internet of Things (IoT) infrastructure to ensure low response time to provision latency-sensitive real-time applications such as health monitoring, disaster management, and smart homes. Fog computing offers a means to provide such requirements, via a virtualized intermediate layer to provide data, computation, storage, and networking services between Cloud datacenters and end users. A key element within such Fog computing environments is resource management. While there are existing resource manager in Fog computing, they only focus on a subset of parameters important to Fog resource management encompassing system response time, network bandwidth, energy consumption and latency. To date no existing Fog resource manager considers these parameters simultaneously for decision making, which in the context of smart homes will become increasingly key. In this paper, we propose a novel resource management technique (ROUTER) for fog-enabled Cloud computing environments, which leverages Particle Swarm Optimization to optimize simultaneously. The approach is validated within an IoT-based smart home automation scenario, and evaluated within iFogSim toolkit driven by empirical models within a small-scale smart home experiment. Results demonstrate our approach results a reduction of 12% network bandwidth, 10% response time, 14% latency and 12.35% in energy consumption.

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References

[1]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things: a survey on enabling technologies, protocols, and applications, IEEE Commun. Surv. Tutor. 17 (4) (2015) 2347–2376.

Digital Library

[2]

L. Atzori, A. Iera, G. Morabito, The internet of things: a survey, Comput. Netw. 54 (15) (2010) 2787–2805.

Digital Library

[3]

R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. De Rose, R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Softw. Pract. Exper. 41 (1) (2011) 23–50.

Digital Library

[4]

C.L.P. Chen, C.-Y. Zhang, Data-intensive applications, challenges, techniques and technologies: a survey on Big Data, Inform. Sci. 275 (2014) 314–347.

[5]

G. Chen, J. Yu, Particle swarm optimization algorithm, Inf. Control-Shenyang 34 (3) (2005) 318.

[6]

R. Deng, R. Lu, C. Lai, T.H. Luan, Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing, in: 2015 IEEE International Conference on Communications (ICC), IEEE, 2015, pp. 3909–3914.

[7]

C.T. Do, N.H. Tran, C. Pham, M.G.R. Alam, J.H. Son, C.S. Hong, A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing, in: 2015 International Conference on Information Networking (ICOIN), IEEE, 2015, pp. 324–329.

[8]

P. Garraghan, R. Yang, Z. Wen, A. Romanovsky, J. Xu, R. Buyya, R. Ranjan, Emergent Failures: rethinking Cloud Reliability at Scale, IEEE Cloud Comput. 5 (5) (2018) 12–21.

[9]

S.S. Gill, R.C. Arya, G.S. Wander, R. Buyya, Fog-based Smart Healthcare as a Big Data and Cloud Service for Heart Patients using IoT, in: International Conference on Intelligent Data Communication Technologies and Internet of Things, Springer, Cham, 2018, pp. 1376–1383.

[10]

S.S. Gill, I. Chana, M. Singh, R. Buyya, CHOPPER: an Intelligent QoS-aware Autonomic Resource Management Approach for Cloud Computing, Clust. Comput. 21 (2) (2018) 1203–1241.

[11]

L. Gu, D. Zeng, S. Guo, A. Barnawi, Y. Xiang, Cost-Efficient Resource Management in Fog Computing Supported Medical CPS, in: IEEE Transactions on Emerging Topics in Computing, 2015, pp. 1–12.

[12]

H. Gupta, A.V. Dastjerdi, S.K. Ghosh, R. Buyya, iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments, Softw. Pract. Exper. 47 (9) (2017) 1275–1296.

[13]

R. Hassan, B. Cohanim, O.D. Weck, G. Venter, A comparison of particle swarm optimization and the genetic algorithm, in: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, 2005, p. 1897.

[14]

A. Kaur, V.P. Singh, S.S. Gill, The Future of Cloud Computing: opportunities, Challenges and Research Trends, in: 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on, IEEE, 2018, August, pp. 213–219.

[15]

W. Lee, K. Nam, H.-G. Roh, S.-H. Kim, A gateway based fog computing architecture for wireless sensors and actuator networks, in: 2016 18th International Conference on Advanced Communication Technology (ICACT), IEEE, 2016, pp. 210–213.

[16]

C. Perera, Y. Qin, J.C. Estrella, S. Reiff-Marganiec, A.V. Vasilakos, Fog computing for sustainable smart cities: a survey, ACM Comput. Surv. (CSUR) 50 (3) (2017) 32.

[17]

M.A. Rodriguez, R. Buyya, Container-based cluster orchestration systems: a taxonomy and future directions, Softw. Pract. Exper. (2018).

[18]

K.C. Sahoo, U.C. Pati, IoT based intrusion detection system using PIR sensor, in: Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017 2nd IEEE International Conference on, IEEE, 2017, pp. 1641–1645.

[19]

S. Singh, I. Chana, A survey on resource scheduling in cloud computing: issues and challenges, J. Grid Comput. 14 (2) (2016) 217–264.

Digital Library

[20]

S. Singh, I. Chana, M. Singh, R. Buyya, SOCCER: self-optimization of energy-efficient cloud resources, Clust. Comput. 19 (4) (2016) 1787–1800.

[21]

J. Son, R. Buyya, Latency-aware Virtualized Network Function provisioning for distributed edge clouds, J. Syst. Softw. 152 (2019) 24–31.

[22]

B.R. Stojkoska, K. Trivodaliev, Enabling internet of things for smart homes through fog computing, in: 2017 25th Telecommunication Forum (TELFOR), Belgrade, 2017, pp. 1–4.

[23]

G. White, V. Nallur, S. Clarke, Quality of service approaches in IoT: a systematic mapping, J. Syst. Softw. 132 (2017) 186–203.

[24]

L. Yu, T. Jiang, Y. Zou, Fog-assisted Operational Cost Reduction For Cloud Data Centers, IEEE, 2017, pp. 13578–13586. Access 5.

[25]

H. Zhang, Y. Zhang, Y. Gu, D. Niyato, Z. Han, A hierarchical game framework for resource management in fog computing, IEEE Commun. Mag. 55 (8) (2017) 52–57.

Cited By

Mohammadi MBahraniPour FEbrahimi Mood SFarshi M(2025)Security-aware resource allocation in fog computing using a meta-heuristic algorithmCluster Computing10.1007/s10586-024-04794-928:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04794-9
Thangaraj VRaja Sree T(2024)MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environmentsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00599-813:1Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1186/s13677-024-00599-8
Mahdizadeh SAbrishami S(2024)An assignment mechanism for workflow scheduling in Function as a Service edge environmentFuture Generation Computer Systems10.1016/j.future.2024.04.003157:C(543-557)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.003
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ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices

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Information & Contributors

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Published In

cover image Journal of Systems and Software

Journal of Systems and Software Volume 154, Issue C

Aug 2019

255 pages

ISSN:0164-1212

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2019

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Cited By

Mohammadi MBahraniPour FEbrahimi Mood SFarshi M(2025)Security-aware resource allocation in fog computing using a meta-heuristic algorithmCluster Computing10.1007/s10586-024-04794-928:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04794-9
Thangaraj VRaja Sree T(2024)MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environmentsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00599-813:1Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1186/s13677-024-00599-8
Mahdizadeh SAbrishami S(2024)An assignment mechanism for workflow scheduling in Function as a Service edge environmentFuture Generation Computer Systems10.1016/j.future.2024.04.003157:C(543-557)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.003
Sha YWang HWang DGhobaei-Arani M(2024)A multi-objective QoS-aware IoT service placement mechanism using Teaching Learning-Based Optimization in the fog computing environmentNeural Computing and Applications10.1007/s00521-023-09246-w36:7(3415-3432)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s00521-023-09246-w
Hajvali MAdabi SRezaee AHosseinzadeh M(2023)Decentralized and scalable hybrid scheduling-clustering method for real-time applications in volatile and dynamic Fog-Cloud EnvironmentsJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00428-412:1Online publication date: 28-Apr-2023
https://dl.acm.org/doi/10.1186/s13677-023-00428-4
Chakraborty AIslam MShahriyar FIslam SZaman HHasan M(2023)Smart Home SystemJournal of Electrical and Computer Engineering10.1155/2023/76166832023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/7616683
Zabihi ZEftekhari Moghadam ARezvani M(2023)Reinforcement Learning Methods for Computation Offloading: A Systematic ReviewACM Computing Surveys10.1145/360370356:1(1-41)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.1145/3603703
Bachiega JCosta BCarvalho LRosa MAraujo A(2023)Computational Resource Allocation in Fog Computing: A Comprehensive SurveyACM Computing Surveys10.1145/358618155:14s(1-31)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3586181
M.R. PH.S. VJ. S(2023)Holistic survey on energy aware routing techniques for IoT applicationsJournal of Network and Computer Applications10.1016/j.jnca.2023.103584213:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jnca.2023.103584
Javanmardi SShojafar MMohammadi RPersico VPescapè A(2023)S-FoSJournal of Information Security and Applications10.1016/j.jisa.2022.10340472:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.jisa.2022.103404
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