Current - Issue
Year 2026 · Volume 5 · Issue 2
Review Article
A Comprehensive Review of Offloading Algorithms in Replicated Fog Computing Environments
Ashish Bagla1
Dr. Deepak Dagar2
Dr. Pratik Shrivastava3
1 Research Scholar, Department of Computer Science and Engineering, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India. 2 Supervisor, Department of Computer Science and Engineering, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India. 3 Assistant Professor, Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India.
Published Online: May-August 2026
Pages: 964-971
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502105
References
1. Meng, L., et al. (2023). Task offloading optimization mechanism based on deep neural network in edge-cloud environment. Journal of
Cloud Computing, 12, Article 76.
2. Tang, L., et al. (2021). Joint optimization of network selection and task offloading for vehicular fog computing. IEEE Internet of Things
Journal, 8(5), 10376-10389.
3. Pakmehr, S., et al. (2024). Exploring future directions for fault-tolerant strategies in replicated fog computing environments. ACM Computing
Surveys, 56(2), 457-490.
4. A. Bhattacharya and P. De, ``A survey of adaptation techniques in computationoffloading,'' J. Netw. Comput. Appl., vol. 78, pp. 97115, Jan.
2017, doi: 10.1016/j.jnca.2016.10.023.
5. K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, ``A survey of computation offloading for mobile systems,'' Mobile Netw. Appl., vol. 18, no.
1, pp. 129140, Apr. 2012, doi: 10.1007/s11036-012-0368-0.
6. A. Yousefpour, G. Ishigaki, and J. P. Jue, ``Fog computing: Towards minimizing delay in the Internet of Things,'' in Proc. IEEE Int. Conf.
Edge Comput. (EDGE), Jun. 2017, pp. 1724.
7. L. Gu, D. Zeng, S. Guo, A. Barnawi, and Y. Xiang, ``Cost efcient resource management in fog computing supported medical cyber-physical
system,'' IEEE Trans. Emerg. Topics Comput., vol. 5, no. 1, pp. 108119, Jan. 2017.
8. R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, ``Optimal workload allocation in fog-cloud computing towards balanced delay and power
consumption,'' IEEE Internet Things J., vol. 3, no. 6, pp. 11711181, Dec. 2016.
9. D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, ``Joint optimization of task scheduling and image placement in fog computing supported
software defined embedded system,'' IEEE Trans. Comput., vol. 65, no. 12, pp. 37023712, Dec. 2016.
10. Y.-C. Chen, Y.-C. Chang, C.-H. Chen, Y.-S. Lin, J.-L. Chen, and Y.-Y. Chang, ``Cloud-fog computing for information-centric Internetof-
Things applications,'' in Proc. Int. Conf. Appl. Syst. Innov. (ICASI), May 2017, pp. 637640.
11. Y.-L. Jiang, Y.-S. Chen, S.-W. Yang, and C.-H. Wu, ``Energy-efficient task offloading for time-sensitive applications in fog computing,''
IEEE Syst. J., vol. 13, no. 3, pp. 29302941, Sep. 2019.
12. M. Huang,W. Liu, T. Wang, A. Liu, and S. Zhang, ``A cloud-MEC collaborative task offloading scheme with service orchestration,''
IEEE Internet Things J., to be published.
13. Y. Liu, Z. Zeng, X. Liu, X. Zhu, and M. Z. A. Bhuiyan, ``A novel load balancing and lowresponse delay framework for edge-cloud network
based on SDN,'' IEEE Internet Things J., to be published.
14. S. Zahoor, N. Javaid, A. Khan, B. Ruqia, F. J. Muhammad, and M. Zahid, ``A Cloud-Fog-Based smart grid model for efcient resource
utilization,'' in Proc. 14th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Jun. 2018, pp. 11541160.
15. S. Zahoor, S. Javaid, N. Javaid, M. Ashraf, F. Ishmanov, and M. Afzal, ``Cloud-fog-based smart grid model for efficient resource
management,'' Sustainability, vol. 10, no. 6, p. 2079, Jun. 2018. [Online]. Available: https://www.mdpi.com/2071-1050/10/6/2079
16. S. A. A. Naqvi, N. Javaid, H. Butt, M. B. Kamal, A. Hamza, and M. Kashif, ``Metaheuristic optimization technique for load balancing in
cloud-fog environment integrated with smart grid,'' in Advances in Network-Based Information Systems,,
17. L. Barolli, N. Kryvinska, T. Enokido, and M. Takizawa, Eds. Cham, Switzerland: Springer, 2019, pp. 700711.
18. S. Ningning, G. Chao, A. Xingshuo, and Z. Qiang, ``Fog computing dynamic load balancing mechanism based on graph repartitioning,''
China Commun., vol. 13, no. 3, pp. 156164, Mar. 2016.
19. S. Bitam, S. Zeadally, and A. Mellouk, ``Fog computing job scheduling optimization based on bees swarm,'' Enterprise Inf. Syst., vol. 12, no.
4, pp. 373397, Apr. 2017, doi: 10.1080/17517575.2017.1304579.
20. H. T. T. Binh, T. T. Anh, D. B. Son, P. A. Duc, and B. M. Nguyen, ``An evolutionary algorithm for solving task scheduling problem in
cloud-fog computing environment,'' in Proc. 9th Int. Symp. Inf. Commun. Technol. (SoICT), New York, NY, USA, 2018, pp. 397404, doi:
10.1145/3287921.3287984.
21. B. M. Nguyen, H. Thi Thanh Binh, T. The Anh, and D. Bao Son, ``Evolutionary algorithms to optimize task scheduling problem for the IoT
based Bag-of-Tasks application in Cloud-Fog computing environment,'' Appl. Sci., vol. 9, no. 9, p. 1730, Apr. 2019.[Online]. Available:
https://www.mdpi.com/2076-3417/9/9/1730
22. A. Mebrek, L. Merghem-Boulahia, and M. Esseghir, ``Efcient green solution for a balanced energy consumption and delay in the IoT-Fog-
Cloud computing,'' in Proc. IEEE 16th Int. Symp. Netw. Comput. Appl. (NCA), Oct. 2017, pp. 14.
23. Y. Li and S. Wang, ``An energy-aware edge server placement algorithm in mobile edge computing,'' in Proc. IEEE Int. Conf. Edge Comput.
(EDGE), Jul. 2018, pp. 6673, doi: 10.1109/edge.2018.00016.
24. C. Canali and R. Lancellotti, ``GASP: Genetic algorithms for service placement in fog computing systems,'' Algorithms, vol. 12, no. 10, p.
201, Sep. 2019. [Online]. Available: https://www.mdpi.com/1999- 4893/12/10/2011
Cloud Computing, 12, Article 76.
2. Tang, L., et al. (2021). Joint optimization of network selection and task offloading for vehicular fog computing. IEEE Internet of Things
Journal, 8(5), 10376-10389.
3. Pakmehr, S., et al. (2024). Exploring future directions for fault-tolerant strategies in replicated fog computing environments. ACM Computing
Surveys, 56(2), 457-490.
4. A. Bhattacharya and P. De, ``A survey of adaptation techniques in computationoffloading,'' J. Netw. Comput. Appl., vol. 78, pp. 97115, Jan.
2017, doi: 10.1016/j.jnca.2016.10.023.
5. K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, ``A survey of computation offloading for mobile systems,'' Mobile Netw. Appl., vol. 18, no.
1, pp. 129140, Apr. 2012, doi: 10.1007/s11036-012-0368-0.
6. A. Yousefpour, G. Ishigaki, and J. P. Jue, ``Fog computing: Towards minimizing delay in the Internet of Things,'' in Proc. IEEE Int. Conf.
Edge Comput. (EDGE), Jun. 2017, pp. 1724.
7. L. Gu, D. Zeng, S. Guo, A. Barnawi, and Y. Xiang, ``Cost efcient resource management in fog computing supported medical cyber-physical
system,'' IEEE Trans. Emerg. Topics Comput., vol. 5, no. 1, pp. 108119, Jan. 2017.
8. R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, ``Optimal workload allocation in fog-cloud computing towards balanced delay and power
consumption,'' IEEE Internet Things J., vol. 3, no. 6, pp. 11711181, Dec. 2016.
9. D. Zeng, L. Gu, S. Guo, Z. Cheng, and S. Yu, ``Joint optimization of task scheduling and image placement in fog computing supported
software defined embedded system,'' IEEE Trans. Comput., vol. 65, no. 12, pp. 37023712, Dec. 2016.
10. Y.-C. Chen, Y.-C. Chang, C.-H. Chen, Y.-S. Lin, J.-L. Chen, and Y.-Y. Chang, ``Cloud-fog computing for information-centric Internetof-
Things applications,'' in Proc. Int. Conf. Appl. Syst. Innov. (ICASI), May 2017, pp. 637640.
11. Y.-L. Jiang, Y.-S. Chen, S.-W. Yang, and C.-H. Wu, ``Energy-efficient task offloading for time-sensitive applications in fog computing,''
IEEE Syst. J., vol. 13, no. 3, pp. 29302941, Sep. 2019.
12. M. Huang,W. Liu, T. Wang, A. Liu, and S. Zhang, ``A cloud-MEC collaborative task offloading scheme with service orchestration,''
IEEE Internet Things J., to be published.
13. Y. Liu, Z. Zeng, X. Liu, X. Zhu, and M. Z. A. Bhuiyan, ``A novel load balancing and lowresponse delay framework for edge-cloud network
based on SDN,'' IEEE Internet Things J., to be published.
14. S. Zahoor, N. Javaid, A. Khan, B. Ruqia, F. J. Muhammad, and M. Zahid, ``A Cloud-Fog-Based smart grid model for efcient resource
utilization,'' in Proc. 14th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), Jun. 2018, pp. 11541160.
15. S. Zahoor, S. Javaid, N. Javaid, M. Ashraf, F. Ishmanov, and M. Afzal, ``Cloud-fog-based smart grid model for efficient resource
management,'' Sustainability, vol. 10, no. 6, p. 2079, Jun. 2018. [Online]. Available: https://www.mdpi.com/2071-1050/10/6/2079
16. S. A. A. Naqvi, N. Javaid, H. Butt, M. B. Kamal, A. Hamza, and M. Kashif, ``Metaheuristic optimization technique for load balancing in
cloud-fog environment integrated with smart grid,'' in Advances in Network-Based Information Systems,,
17. L. Barolli, N. Kryvinska, T. Enokido, and M. Takizawa, Eds. Cham, Switzerland: Springer, 2019, pp. 700711.
18. S. Ningning, G. Chao, A. Xingshuo, and Z. Qiang, ``Fog computing dynamic load balancing mechanism based on graph repartitioning,''
China Commun., vol. 13, no. 3, pp. 156164, Mar. 2016.
19. S. Bitam, S. Zeadally, and A. Mellouk, ``Fog computing job scheduling optimization based on bees swarm,'' Enterprise Inf. Syst., vol. 12, no.
4, pp. 373397, Apr. 2017, doi: 10.1080/17517575.2017.1304579.
20. H. T. T. Binh, T. T. Anh, D. B. Son, P. A. Duc, and B. M. Nguyen, ``An evolutionary algorithm for solving task scheduling problem in
cloud-fog computing environment,'' in Proc. 9th Int. Symp. Inf. Commun. Technol. (SoICT), New York, NY, USA, 2018, pp. 397404, doi:
10.1145/3287921.3287984.
21. B. M. Nguyen, H. Thi Thanh Binh, T. The Anh, and D. Bao Son, ``Evolutionary algorithms to optimize task scheduling problem for the IoT
based Bag-of-Tasks application in Cloud-Fog computing environment,'' Appl. Sci., vol. 9, no. 9, p. 1730, Apr. 2019.[Online]. Available:
https://www.mdpi.com/2076-3417/9/9/1730
22. A. Mebrek, L. Merghem-Boulahia, and M. Esseghir, ``Efcient green solution for a balanced energy consumption and delay in the IoT-Fog-
Cloud computing,'' in Proc. IEEE 16th Int. Symp. Netw. Comput. Appl. (NCA), Oct. 2017, pp. 14.
23. Y. Li and S. Wang, ``An energy-aware edge server placement algorithm in mobile edge computing,'' in Proc. IEEE Int. Conf. Edge Comput.
(EDGE), Jul. 2018, pp. 6673, doi: 10.1109/edge.2018.00016.
24. C. Canali and R. Lancellotti, ``GASP: Genetic algorithms for service placement in fog computing systems,'' Algorithms, vol. 12, no. 10, p.
201, Sep. 2019. [Online]. Available: https://www.mdpi.com/1999- 4893/12/10/2011
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