research-article

Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing

Authors:

Jin Yu Academic Editor:

Fuhong LinAuthors Info & Claims

Wireless Communications and Mobile Computing, Volume 2018

https://doi.org/10.1155/2018/2734219

Published: 01 January 2018 Publication History

Abstract

Fog computing has changed the distributed computing rapidly by including the smart devices widely distributed at the network edges. It is able to provide less latency and is more capable of decreasing traffic jam in the network. However, it will bring more difficulties for resource managing and task scheduling especially in a decentralized ad hoc network. In this paper, we propose a method that takes advantages of the immune mechanism to schedule tasks in a decentralized way for fog computing. By using forward propagation and backward propagation in the ad hoc network, the power of distributed schedulers is used to generate the optimized scheduler strategies to deal with computing nodes overloaded and achieve the optimal task finishing time reducing. The experiment results show that our approach can beat similar methods.

References

[1]

L. F. Bittencourt, J. Diaz-Montes, R. Buyya, O. F. Rana, and M. Parashar, “Mobility-Aware Application Scheduling in Fog Computing,” IEEE Cloud Computing, vol. 4, no. 2, pp. 26–35, 2017.

[2]

C. Mouradian et al., “A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges,” IEEE Communications Surveys & Tutorials, vol. 99, p. 1, 2017.

[3]

K. A. Hummel and G. Jelleschitz, “A robust decentralized job scheduling approach for mobile peers in ad-hoc grids,” in Proceedings of the 7th IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2007, pp. 461–468, May 2007.

[4]

M. Sanei and N. M. Charkari, “Hybrid heuristic-based artificial immune system for task scheduling,” International Journal of Distributed & Parallel Systems, vol. 2, no. 6, 2011.

[5]

Y.-K. Kwok and I. Ahmad, “Static scheduling algorithms for allocating directed task graphs to multiprocessors,” ACM Computing Surveys, vol. 31, no. 4, pp. 406–471, 1999.

Digital Library

[6]

B. Kruatrachue and T. Lewis, “Grain size determination for parallel processing,” IEEE Software, vol. 5, no. 1, pp. 23–32, 1988.

Digital Library

[7]

G. C. Sih and E. A. Lee, “Compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures,” IEEE Transactions on Parallel and Distributed Systems, vol. 4, no. 2, pp. 175–187, 1993.

Digital Library

[8]

M. Wu and D. D. Gajski, “Hypertool: a programming aid for message-passing systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 1, no. 3, pp. 330–343, 1990.

Digital Library

[9]

H. El-Rewini and T. G. Lewis, “Scheduling parallel program tasks onto arbitrary target machines,” Journal of Parallel and Distributed Computing, vol. 9, no. 2, pp. 138–153, 1990.

Digital Library

[10]

Y. Kwok and I. Ahmad, “Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors,” IEEE Transactions on Parallel and Distributed Systems, vol. 7, no. 5, pp. 506–521, 1996.

Digital Library

[11]

R. Hwang, M. Gen, and H. Katayamaa, “A comparison of multiprocessor task scheduling algorithms with communication costs,” Computers & Operations Research, vol. 35, no. 3, pp. 976–993, 2008.

Digital Library

[12]

T. Yang and A. Gerasoulis, “DSC: scheduling parallel tasks on an unbounded number of processors,” IEEE Transactions on Parallel and Distributed Systems, vol. 5, no. 9, pp. 951–967, 1994.

Digital Library

[13]

Y. C. Lee and A. Y. Zomaya, “An Artificial Immune System for Heterogeneous Multiprocessor Scheduling with Task Duplication,” in Proceedings of the 2007 IEEE International Parallel and Distributed Processing Symposium, pp. 1–8, Long Beach, CA, USA, March 2007.

[14]

D. Dasgupta and F. Nino, “Immunological computation : theory and applications,” in Longman, p. 140, 2008.

[15]

N. K. Jerne, “Towards a network theory of the immune system,” Annales Dimmunologie, vol. 125, no. (1-2), p. 373, 1974.

[16]

Y. Huang, N. Bessis, P. Norrington, P. Kuonen, and B. Hirsbrunner, “Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm,” Future Generation Computer Systems, vol. 29, no. 1, pp. 402–415, 2013.

Digital Library

[17]

P. Ramachandran, B. Zoph, and Q. V. Le, Searching for Activation Functions, 2017.

[18]

I. H. Witten and E. Frank, Data Mining. Practical Machine Learning Tools & Techniques with Java Implementations, vol. 13, 2005.

[19]

B. Karlik and A. V. Olgac, Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks, 42, Cambridge University Press, 2010.

[20]

D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning,” Choice Reviews Online, vol. 27, no. 02, pp. 27-0936–27-0936, 1989.

[21]

W. Gong, Á. Fialho, Z. Cai, and H. Li, “Adaptive strategy selection in differential evolution for numerical optimization: an empirical study,” Information Sciences, vol. 181, no. 24, pp. 5364–5386, 2011.

Digital Library

[22]

Q. Lin et al., “A novel hybrid multi-objective immune algorithm with adaptive differential evolution,” Computers & Operations Research, vol. 62, pp. 95–111, 2015.

Digital Library

[23]

“Standard Task Graph Set,” http://www.kasahara.elec.waseda.ac.jp/schedule.

Cited By

Al-Qadhi ALatip RChiong RAthauda RHussin M(2025)Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic reviewCluster Computing10.1007/s10586-024-04771-228:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04771-2
Mohamed AAbdellatif AAlburaikan AKhalifa HElaziz MAbualigah LAbdelMouty A(2023)A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07805-227:9(5769-5780)Online publication date: 18-Jan-2023
https://dl.acm.org/doi/10.1007/s00500-022-07805-2
Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Show More Cited By

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Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing

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Information & Contributors

Information

Published In

cover image Wireless Communications & Mobile Computing

Wireless Communications & Mobile Computing Volume 2018, Issue

2018

6447 pages

ISSN:1530-8669

Issue’s Table of Contents

Copyright © 2018 Yabin Wang et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 January 2018

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Cited By

Al-Qadhi ALatip RChiong RAthauda RHussin M(2025)Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic reviewCluster Computing10.1007/s10586-024-04771-228:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04771-2
Mohamed AAbdellatif AAlburaikan AKhalifa HElaziz MAbualigah LAbdelMouty A(2023)A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07805-227:9(5769-5780)Online publication date: 18-Jan-2023
https://dl.acm.org/doi/10.1007/s00500-022-07805-2
Jamil BIjaz HShojafar MMunir KBuyya R(2022)Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/351300254:11s(1-38)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3513002
Singh RAwasthi LSikka G(2022)Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic ReviewACM Computing Surveys10.1145/349452055:3(1-43)Online publication date: 3-Feb-2022
https://dl.acm.org/doi/10.1145/3494520
Yadav ATripathi KSharma S(2022)An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environmentCluster Computing10.1007/s10586-021-03481-325:2(983-998)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10586-021-03481-3

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