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Fusion: Practice and Applications
Volume 8 , Issue 2, PP: 25-35 , 2022 | Cite this article as | XML | Html |PDF


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

Authors Names :   Joseph B. Awotunde   1 *     Hrudaya K. Tripathy   2     Anjan Bandyopadhyay   3  

1  Affiliation :  Faculty of Information and Communication Sciences, University of Ilorin, Nigeria

    Email :  awotunde.jb@unilorin.edu.ng

2  Affiliation :  School of Computer Engineering, Kalinga Institute of Industrial Technology, India

    Email :  hktripathyfcs@kiit.ac.in

3  Affiliation :  Kalinga Institute of Industrial Technology (KIIIT) Bhubaneswar, Odisha, India

    Email :  anjan.bandyopadhyayfcs@kiit.ac.in

Doi   :   https://doi.org/10.54216/FPA.080203

Received: May 11, 2022 Accepted: September 17, 2022

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-


[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-


[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.

Cite this Article as :
Joseph B. Awotunde , Hrudaya K. Tripathy , Anjan Bandyopadhyay, Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms, Fusion: Practice and Applications, Vol. 8 , No. 2 , (2022) : 25-35 (Doi   :  https://doi.org/10.54216/FPA.080203)