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Fusion: Practice and Applications

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Online: 2692-4048 Print: 2770-0070
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Fusion: Practice and Applications
Full Length Article

Volume 8 • Issue 2 • PP: 25-35 • 2022

Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms

Joseph B. Awotunde 1*
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,
Hrudaya K. Tripathy 2
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,
Anjan Bandyopadhyay 3
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1Faculty of Information and Communication Sciences, University of Ilorin, Nigeria
2School of Computer Engineering, Kalinga Institute of Industrial Technology, India
3Kalinga Institute of Industrial Technology (KIIIT) Bhubaneswar, Odisha, India
* Corresponding Author.
DOI https://doi.org/10.54216/FPA.080203
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Received: May 11, 2022 Accepted: September 17, 2022
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Abstract

The recent wide acceptance of cloud and virtualization technologies has made a number of Internet of Things (IoT) applications practical. Although these technologies are typically useful, they may introduce a high transmission latency in IoT environments, e.g., data fusion in smart cities. To address this issue, fog computing, a distributed decentralized computing layer between IoT hardware and the cloud layer, can be used. To facilitate the use of fog computing in IoT data fusion environments, this paper proposes a new Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique (HPSOFF-RPT) model for fog-cloud computing platforms. The HPSOFF-RPT model is designed to optimize resource allocation and distribution in IoT environments. The model uses the  PSO and FF algorithms to provision resources in the fog-cloud environment. To evaluate performance, a wide-ranging simulation analysis is performed. The simulation results show that the proposed model improves performance compared to the existing optimization algorithms.

Keywords

Resource provisioning Fog computing Cloud computing Hybrid metaheuristics Data Fusion

References

[1] Duc, T.L., Leiva, R.G., Casari, P. and Östberg, P.O., 2019. Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey. ACM Computing Surveys (CSUR), 52(5), pp.1- 39.

[2] Varshney, S., Sandhu, R. and Gupta, P.K., 2019, April. QoS based resource provisioning in cloud computing environment: a technical survey. In International conference on advances in computing and data sciences (pp. 711-723). Springer, Singapore.

[3] Vinothiyalakshmi, P. and Anitha, R., 2021. Efficient dynamic resource provisioning based on credibility in cloud computing. Wireless Networks, 27(3), pp.2217-2229.

[4] Suresh, A. and Varatharajan, R., 2019. Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Computing, 22(5), pp.11039-11046.

[5] Aslanpour, M.S., Dashti, S.E., Ghobaei-Arani, M. and Rahmanian, A.A., 2018. Resource provisioning for cloud applications: a 3-D, provident and flexible approach. The Journal of Supercomputing, 74(12), pp.6470-6501.

[6] Debbi, H., 2021. Modeling and Performance Analysis of Resource Provisioning in Cloud Computing using Probabilistic Model Checking. Informatica, 45(4).

[7] Saxena, D. and Singh, A.K., 2022. OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments. The Journal of Supercomputing, pp.1-22.

[8] Senturk, I.F., Balakrishnan, P., Abu-Doleh, A., Kaya, K., Malluhi, Q. and Çatalyürek, Ü.V., 2018. A resource provisioning framework for bioinformatics applications in multi-cloud environments. Future Generation Computer Systems, 78, pp.379-391.

[9] Shakarami, A., Shakarami, H., Ghobaei-Arani, M., Nikougoftar, E. and Faraji-Mehmandar, M., 2022. Resource provisioning in edge/fog computing: A Comprehensive and Systematic Review. Journal of Systems Architecture, 122, p.102362.

[10] Santos, J., Wauters, T., Volckaert, B. and De Turck, F., 2021. Towards end-to-end resource provisioning in fog computing over low power wide area networks. Journal of Network and Computer Applications, 175, p.102915.

[11] Khorsand, R., Ghobaei‐Arani, M. and Ramezanpour, M., 2019. A self‐learning fuzzy approach for proactive resource provisioning in cloud environment. Software: Practice and Experience, 49(11), pp.1618- 1642.

[12] Bibal Benifa, J.V. and Dejey, D., 2019. Rlpas: Reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment. Mobile Networks and Applications, 24(4), pp.1348-1363.

[13] Ghobaei-Arani, M., Khorsand, R. and Ramezanpour, M., 2019. An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. Journal of Network and Computer Applications, 142, pp.76-97

[14] Shahidinejad, A., Ghobaei-Arani, M. and Masdari, M., 2021. Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Cluster Computing, 24(1), pp.319-342

[15] Ghobaei-Arani, M., Jabbehdari, S. and Pourmina, M.A., 2018. An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems, 78, pp.191-210

[16] Rajasekar, P. and Palanichamy, Y., 2022. A flexible deadline-driven resource provisioning and scheduling algorithm for multiple workflows with VM sharing protocol on WaaS-cloud. The Journal of Supercomputing, pp.1-31

[17] Kennedy, J. and Eberhart, R., 1995, November. Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.

[18] Yang, X.S., 2010. Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv:1003.1409.

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Awotunde, Joseph B., Tripathy, Hrudaya K., Bandyopadhyay, Anjan. "Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms." Fusion: Practice and Applications, vol. Volume 8, no. Issue 2, 2022, pp. 25-35. DOI: https://doi.org/10.54216/FPA.080203
Awotunde, J., Tripathy, H., Bandyopadhyay, A. (2022). Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms. Fusion: Practice and Applications, Volume 8(Issue 2), 25-35. DOI: https://doi.org/10.54216/FPA.080203
Awotunde, Joseph B., Tripathy, Hrudaya K., Bandyopadhyay, Anjan. "Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms." Fusion: Practice and Applications Volume 8, no. Issue 2 (2022): 25-35. DOI: https://doi.org/10.54216/FPA.080203
Awotunde, J., Tripathy, H., Bandyopadhyay, A. (2022) 'Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms', Fusion: Practice and Applications, Volume 8(Issue 2), pp. 25-35. DOI: https://doi.org/10.54216/FPA.080203
Awotunde J, Tripathy H, Bandyopadhyay A. Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms. Fusion: Practice and Applications. 2022;Volume 8(Issue 2):25-35. DOI: https://doi.org/10.54216/FPA.080203
J. Awotunde, H. Tripathy, A. Bandyopadhyay, "Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms," Fusion: Practice and Applications, vol. Volume 8, no. Issue 2, pp. 25-35, 2022. DOI: https://doi.org/10.54216/FPA.080203
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