Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Research Article

Research on Fog Resource Scheduling based on Cloud-fog Collaboration Technology in the Electric Internet of Things

Author(s): Youchan Zhu, Yingzi Wang* and Weixuan Liang

Volume 14, Issue 3, 2021

Published on: 04 January, 2021

Page: [347 - 359] Pages: 13

DOI: 10.2174/2352096514999210104144312

Price: $65

Abstract

Background: With the further development of the electric Internet of Things (eIoT), IoT devices in the distributed network generate data with different frequencies and types.

Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time.

Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through the K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm.

Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate, and energy consumption, which provides a better guarantee for eIoT.

Conclusion: Resource scheduling is an important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. A new method of resource scheduling and load balancing is proposed. The evaluation was performed, and it proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

Keywords: Power grid Internet of things, fog computing, data management, data cleaning, date integration, data storage.

« Previous Next »
Graphical Abstract

[1]
Y. Liu, "Study of data integration architecture for widearea distributed power quality of power grid", In: 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), Shanghai, China, 2018, pp. 1-6.
[http://dx.doi.org/10.1109/ISSI.2018.8538098]
[2]
L. Jin, L. Haosong, X. Zhongping, W. Ting, W. Shuai, W. Yutong, H. Dongliang, K. Chunting, W. Jia, and S. Dan, "Research on wide-area distributed power quality data fusion technology of power grid", In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, 2019.
[http://dx.doi.org/10.1109/ICCCBDA.2019.8725668]
[3]
L. Sun, K. Zhou, X. Zhang, and S. Yang, "Outlier data treatment methods toward smart grid applications", IEEE Access, vol. 6, pp. 39849-39859, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2852759]
[4]
J. Dai, H. Song, G. Sheng, and X. Jiang, "Cleaning method for status monitoring data of power equipment based on stacked denoising autoencoders", IEEE Access, vol. 5, pp. 22863-22870, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2740968]
[5]
L. Wang, and Q. Liang, "Representation learning and nature encoded fusion for heterogeneous sensor networks", IEEE Access, vol. 7, pp. 39227-39235, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2907256]
[6]
Y. Ma, Z. Guo, Y. Chen, and L. Zou, "Multi-sourced data storage and index construction for equipment condition assessment", In: 2014 International Conference on Computational Intelligence and Communication Networks, Bhopal, India, 2014, pp. 681-685.
[http://dx.doi.org/10.1109/CICN.2014.150]
[7]
B. Lu, and W. Song, "Research on heterogeneous data integration for Smart Grid", In: 2010 3rd International Conference on Computer Science and Information Technology., Chengdu, China, 2010, pp. 52-56.
[8]
L. Zhang, Y. Xie, L. Xidao, and X. Zhang, "Multi-source heterogeneous data fusion", In: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, India, 2018, pp. 47-51.
[http://dx.doi.org/10.1109/ICAIBD.2018.8396165]
[9]
P. Chen, J. Liu, X. Liu, R. Zheng, and Y. Pan, "Research on tiered storage method for big data of virtual information based on cloud computing", In: 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), Xiangtan, China, 2019, pp. 308-311.
[http://dx.doi.org/10.1109/ICSGEA.2019.00077]
[10]
U. Das, and V. Namboodiri, "A quality-aware multi-level data aggregation approach to manage smart grid AMI traffic", IEEE Trans. Parallel Distrib. Syst., vol. 30, no. 2, pp. 245-256, 2019.
[http://dx.doi.org/10.1109/TPDS.2018.2865937]
[11]
K. Wang, "Network data management model based on Naïve Bayes classifier and deep neural networks in heterogeneous wireless networks", Computers Electrical Engineering, vol. 75, pp. 135-145, 2019.
[12]
Z. Song, Y. Sun, J. Wan, and P. Liang, "Data quality management for service-oriented manufacturing cyber-physical systems", Comput. Electric. Eng., vol. 64, pp. 34-44, 2017.
[13]
J. Fu, Y. Liu, H. Chao, B.K. Bhargava, and Z. Zhang, "Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing", IEEE Trans. Industr. Inform., vol. 14, no. 10, pp. 4519-4528, 2018.
[http://dx.doi.org/10.1109/TII.2018.2793350]
[14]
J.I. Guerrero, "Heterogeneous data source integration for smart grid ecosystems based on metadata mining", Expert Syst. Appl., vol. 79, pp. 254-268, 2017.
[http://dx.doi.org/10.1016/j.eswa.2017.03.007]
[15]
K. Jia, X.M. Ju, and H.B. Zhang, "Research on big data fusion method of smart grid in the environment of internet of things", In: 4th International Conference on Cloud Computing and Security (ICCCS), 2018.
[http://dx.doi.org/10.1007/978-3-030-00018-9_56]
[16]
M. Cosovic, A. Tsitsimelis, D. Vukobratovic, and J. Matamoros, "5G mobile cellular networks: Enabling distributed state estimation for smart grids"IEEE Commun. Mag., vol. 55. 2017, no. 10, pp. 62-69.
[17]
D. Rahbari, and M. Nickray, "Scheduling of Fog networks with optimized knapsack symbiotic organisms search", In: Proceeding of the 21st Conference of Fruct. Association, 2018.
[18]
J. Ma, "Resource management framework for virtual data center embedding based on software defined networking", Comput. Electric. Eng., vol. 60, pp. 76-89, 2017.
[19]
L.F. Bittencourt, J. Diaz-Montes, R. Buyya, F.R. Omer, and P. Manish, "Mobility-aware application scheduling in Fog computing", IEEE Cloud Comput., vol. 4, no. 2, pp. 26-35, 2017.
[http://dx.doi.org/10.1109/MCC.2017.27]
[20]
S.H.H. Madni, M.S.A. Latiff, M. Abdullahi, S.M. Abdulhamid, and M.J. Usman, "Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment", PLoS One, vol. 12, no. 5, 2017.
[http://dx.doi.org/10.1371/journal.pone.0176321] [PMID: 28467505]
[21]
Y. Gao, H. Guan, Z. Qi, T. Song, F. Huan, and L. Liu, "Service level agreement based energy-efficient resource management in cloud data centers", Comput. Electric. Eng., vol. 40, no. 5, pp. 1621-1633, 2014.
[22]
X.Q. Pham, and E. Huh, "Towards task scheduling in a cloud-fog computing system", In: 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2016.
[23]
T. Choudhari, M. Moh, and T. Moh, "Prioritized task scheduling in Fog computing", In: ACMSE Conference Transactions Proceedings, 2018.
[24]
T.D. Dang, and D. Hoang, "FBRC: Optimization of task scheduling in Fog based region and cloud", IEEE Trustcom/BigDataSE/ICESS., 2017.
[25]
M.P. Shelke, A. Malhotra, and P. Mahalle, "A packet priority intimation-based data transmission for congestion free traffic management in wireless sensor networks", Comput. Electric. Eng., vol. 64, pp. 248-261, 2017.
[26]
S. Sharma, and H. Saini, "A novel fourtier architecture for delay aware scheduling and load balancing in fog environment", Sustain. Comput.: Inform. Syst., vol. 24, p. 100355, 2019.
[27]
M. Mukherjee, Q. Zhang, R. Matam, C. Mavromoustakis, Y. Lv, G. Mastorakis, L. Constandinos, and G. Yunrong, "Task data offloading and resource allocation in Fog computing with multi-task delay guarantee", IEEE Access, pp. 1-1, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2941741]
[28]
J. Liu, and Q. Zhang, "Offloading schemes in mobile edge computing for ultra-reliable low latency communications", IEEE Access, vol. 6, pp. 12825-12837, 2018.
[29]
X. Pham, N.D. Man, N.D.T. Tri, N.Q. Thai, and E. Huh, "A Cost and performance effective approach for task scheduling based on collaboration between cloud and Fog computing", Int. J. Distrib. Sens. Netw., vol. 13, pp. 1-16, 2017.
[http://dx.doi.org/10.1177/1550147717742073]
[30]
Y. Yang, S. Zhao, W. Zhang, Y. Chen, X. Luo, and J. Wang, DEBTS: Delay energy balanced task scheduling in homogeneous Fog networks., IEEE Internet of Things J., pp. 1-11, 2018.
[31]
S. Bitam, S. Zeadally, and A. Mellouk, "Fog computing job scheduling optimization based on bees swarm", Enterprise Inf. Syst., pp. 373-397, 2017.
[32]
S. Ningning, G. Chao, A. Xingshuo, and Z. Qiang, "Fog computing dynamic load balancing mechanism based on graph repartitioning", China Commun., pp. 156-164, 2016.
[http://dx.doi.org/10.1109/CC.2016.7445510]
[33]
Y. Su, F. Lin, and H. Xu, "Multi-objective optimization of resource scheduling in Fog computing using an improved NSGA-II", Wirel. Pers. Commun., vol. 102, pp. 1369-1385, 2017.
[34]
H. Gupta, A.V. Dastjerdi, S.K. Ghosh, and R. Buyya, "iFogSim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and Fog computing environments", Software Practice Expertise, WILEY., vol. 47, pp. 1275-1296, 2017.
[http://dx.doi.org/10.1002/spe.2509]
[35]
H.F. Sheikh, I. Ahmad, and D. Fan, "An evolutionary technique for performance-energy-temperature optimized scheduling of parallel tasks on multi-core processors", IEEE Transact. Parallel Distribut. Syst., vol. 27, no. 3, pp. 668-681, 2016.
[36]
H.F. Sheikh, I. Ahmad, Z. Wang, and S. Ranka, "An overview and classification of thermal-aware scheduling techniques for multi-core processing systems, sustainable computing", Inf. Syst., vol. 2, no. 3, pp. 151-169, 2012.
[37]
C.C. Kai, C.Y. Hao, Yang Chih-Chieh, and Lee Jenq-Kuen, "Switching supports for stateful object remoting on network processors", J. Super Comput., vol. 40, no. 3, pp. 281-298, 2007.
[38]
H. Topcuoglu, S. Hariri, and M-Y. Wu, "Performance effective and low-complexity task scheduling for heterogeneous computing", IEEE Transactions on Parallel and Distributed Systems, vol. 13, no. 3, pp. 260-274, 2002.
[39]
T. Wang, Z. Liu, Y. Chen, and Y. Xu, "Load balancing task scheduling based on genetic algorithm in cloud computing", In: 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing., 2014.

Rights & Permissions Print Cite
Article Metrics
6
1
© 2024 Bentham Science Publishers | Privacy Policy