International Journal of Computer Networks and Applications (IJCNA)

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ISSN : 2395-0455

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Improved Performance of Cloud Networks Using Chaotic Black Widow Optimization Algorithm

Author NameAuthor Details

Alaa Mokhtar, Hegazy Zaher, Naglaa Ragaa, Eman Mostafa

Alaa Mokhtar[1]

Hegazy Zaher[2]

Naglaa Ragaa[3]

Eman Mostafa[4]

[1]Department of Operations Research and Management, Cairo University, Giza, Egypt

[2]Department of Mathematical Statistics, Cairo University, Giza, Egypt

[3]Department of Operations Research and Management, Cairo University, Giza, Egypt

[4]Department of Operations Research and Management, Cairo University, Giza, Egypt

Cite this Article

DOI: 10.22247/ijcna/2022/215923

Pages : 653-660

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Abstract

Nowadays, the speed of the internet becomes very high and users use a lot of data from a data center, so users need to access a lot of data from the nearest place to avoid any delay. The data will be stored without any interference from users. Due to the previous reasons, Cloud Computing (CC) plays an important role in finding the best route for data to reach users by general networks without a delay and with the least energy consumption. To achieve scalability, protocols are used and developed for networks for a suitable performance in the cloud computing networks. In this paper, a natural-inspired protocol is used in cloud networks to find a good routing, this protocol is called Chaotic Black Widow Optimization Algorithm (CBWOA). Some natural characteristics of the routing strategy are inspired by the black widow optimization algorithm. Taguchi's methods were used for tuning parameters of CBWOA based on orthogonal array experiments. CBWOA is applied with three constraints to measure two factors called energy consumption and throughput by finding best path direction in cloud Computing networks. CBWOA is evaluated in an experimental cloud using the current routing protocols against old routing protocols of the performance network. Finally, the best route in cloud networks is founded successfully and the performance of the developed protocol is associated with two factors, the first one is maximizing (the throughput with efficiency 93.4921% and the Packet delivery ratio with efficiency 90.3087%) and the second one is minimizing (the delay with efficiency 62.1788% and the energy consumed with efficiency 51.1406%).

Index Terms

Optimization

Black Widow Optimization Algorithm

Taguchi's Methods

CBWOA

Internet

Cloud Networks

Reference

  1. 1.
    Aydin, H., & Sertba?, A, “Cyber security in Industrial Control Systems (ics): a survey of rowhammer vulnerability”. Applied Computer Science, 18(2), 2022, 86-100. https://doi.org/10.35784/acs-2022-15
  2. 2.
    Ramkumar, J., Vadivel, R., & Narasimhan, B, “Constrained Cuckoo Search Optimization Based Protocol for Routing in Cloud Network”. International Journal of Computer Networks and Applications, 2021, 8(6), 795. DOI: 10.22247/ijcna/2021/210727
  3. 3.
    Faheem, M., Butt, R. A., Ali, R., Raza, B., Ngadi, M. A., & Gungor, V. C, “CBI4. 0: A cross-layer approach for big data gathering for active monitoring and maintenance in the manufacturing industry 4.0”. Journal of Industrial Information Integration, 2021, 24, 100236. https://doi.org/10.1016/j.jii.2021.100236
  4. 4.
    Huang, C. Y., & Chang, Y. J, “An adaptively multi-attribute index framework for big IoT data”. Computers & Geosciences, 2021, 155, 104841. https://doi.org/10.1016/j.cageo.2021.104841
  5. 5.
    Hoy, R. R, “Quantitative skills in undergraduate neuroscience education in the age of big data. Neuroscience Letters”, 2021, 759, 136074. https://doi.org/10.1016/j.neulet.2021.136074
  6. 6.
    Martínez, P. L., Dintén, R., Drake, J. M., & Zorrilla, M, “A big data-centric architecture metamodel for Industry 4.0”. Future Generation Computer Systems, 2021, 125, 263-284. https://doi.org/10.1016/j.future.2021.06.020
  7. 7.
    Nilashi, M., Ahmadi, H., Arji, G., Alsalem, K. O., Samad, S., Ghabban, F., & Alarood, A. A, “Big social data and customer decision making in vegetarian restaurants: A combined machine learning method”. Journal of Retailing and Consumer Services, 2021, 62, 102630. https://doi.org/10.1016/j.jretconser.2021.102630
  8. 8.
    Mokhtar, A., Hegazy Zaher, N. R., & Mostafa, E, “A Black Widow Optimization Algorithm for Clustering Problems. Journal of University of Shanghai for Science and Technology”, 2021, 82-93. DOI: 10.51201/JUSST/21/06506
  9. 9.
    Omar, H. A., Zhuang, W., & Li, L, “Gateway placement and packet routing for multihop in-vehicle internet access”. IEEE Transactions on Emerging Topics in Computing, 2015, 3(3), 335-351. DOI: 10.1109/TETC.2015.2395077
  10. 10.
    Wang, C., Zhang, L., Li, Z., & Jiang, C, “SDCoR: software defined cognitive routing for internet of vehicles”. IEEE Internet of Things Journal, 2018, 5(5), 3513-3520. DOI: 10.1109/JIOT.2018.2812210
  11. 11.
    Long, N. B., Tran-Dang, H., & Kim, D. S, “Energy-aware real-time routing for large-scale industrial internet of things”. IEEE Internet of Things Journal, 2018, 5(3), 2190-2199. DOI: 10.1109/JIOT.2018.2827050
  12. 12.
    Xu, Y., Yue, Z., & Lv, L, “Clustering routing algorithm and simulation of internet of things perception layer based on energy balance”. IEEE Access, 7, 2019, 145667-145676. DOI: 10.1109/ACCESS.2019.2944669
  13. 13.
    Zhao, L., Bi, Z., Lin, M., Hawbani, A., Shi, J., & Guan, Y, “An intelligent fuzzy-based routing scheme for software-defined vehicular networks”. Computer Networks, 2021, 187, 107837. https://doi.org/10.1016/j.comnet.2021.107837
  14. 14.
    Golchi, Mahya Mohammadi, Shideh Saraeian, and Mehrnoosh Heydari. "A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation." Computer Networks 162 (2019): 106860. https://doi.org/10.1016/j.comnet.2019.106860
  15. 15.
    Hayyolalam, V., & Kazem, A. A. P, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems”. Engineering Applications of Artificial Intelligence, 2020, 87, 103249. https://www.sciencedirect.com/science/article/abs/pii/S0952197619302283
  16. 16.
    Scott, C., Kirk, D., McCann, S., & Gries, G, “Web reduction by courting male black widow’s renders pheromone-emitting females' webs less attractive to rival males. Animal Behaviour”, 2015, 107, 71-78. https://doi.org/10.1016/j.anbehav.2015.06.009
  17. 17.
    Mokhtar, A., Hegazy Zaher, N. R., & Mostafa, E, “A Black Widow Optimization Algorithm Using Chaos Reproduction Operator”. Design Engineering, 2021, 4751-4763. http://www.thedesignengineering.com/index.php/DE/article/view/5426
  18. 18.
    Premkumar, K., Vishnupriya, M., Sudhakar Babu, T., Manikandan, B. V., Thamizhselvan, T., Nazar Ali, A., ... & Parvez Mahmud, M. A, “Black widow optimization-based optimal PI-controlled wind turbine emulator”. Sustainability, 2020, 12(24), 10357. https://doi.org/10.3390/su122410357
  19. 19.
    Munagala, V. K., & Jatoth, R. K, “Improved fractional PI?D? controller for AVR system using Chaotic Black Widow algorithm”. Computers & Electrical Engineering, 2022, 97, 107600. https://doi.org/10.1016/j.compeleceng.2021.107600
  20. 20.
    Ghani, J. A., Choudhury, I. A., & Hassan, H. H, “Application of Taguchi method in the optimization of end milling parameters”. Journal of Materials Processing Technology, 2004, 145(1), 84–92. https://doi.org/10.1016/S0924-0136(03)00865-3
  21. 21.
    Mozdgir, A., Mahdavi, I., Badeleh, I. S., & Solimanpur, M, “Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing”. Mathematical and Computer Modelling, 2013, 57(1-2), 137-151. https://doi.org/10.1016/j.mcm.2011.06.056
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