Optimizing of Cloud Storage Performance by Using Enhanced Clustering Technology

Ahmed Nafea Ayesh
International Journal of Computational and Electronic Aspects in Engineering
Volume 5: Issue 1, January 2024, pp 16-24

Author's Information
Ahmed Nafea Ayesh 1 
Corresponding Author
1Al-Iraqia University, Baghdad, Iraq
mfmsprof@utq.edu.iq
Research Paper -- Peer Research Papered
First online on – 30 March 2024

Open Access article under Creative Commons License

Cite this article –Ahmed Nafea Ayesh “Optimizing of Cloud Storage Performance by Using Enhanced Clustering Technology”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 5, Issue 1, pp. 16-24, 2024.
https://doi.org/10.26706/ijceae.5.1.20240503

Abstract:-
Optimizing the performance of cloud storage is still one of the most urgent research subjects when it comes to advanced computing. This paper deals with the problem of low productivity in existing cloud storage operations by introducing a powerful clustering solution that aims at changing the existing technology. The proposed technique is based on an automation where data is automatically classified on various criteria giving rise to wider applicability for the benefit of its users while the scalability, security, and cost-effectiveness is increased. This method dynamically adopts to infrastructure requirements; hence the approach overcomes the existing barriers and additionally, it accommodates the new business needs more optimally. In addition, it is the main method for the safekeeping of confidential data such as unique inventions or financial data by using proper security measures. It enhances the efficiency of the storage grid by allocating the resources effectively as well as automating the data path to the most optimal places based on the user behavior and business needs. Cloud computing has been an increasingly significant consideration in many businesses, therefore, because this clustering technology is a key enabler, it has been in high demand by users.
Index Terms:-
Workload, Cloud storage, Performance optimization, Data organization, K-means, Scalability, Cost-effectiveness
REFERENCES
  1. M. Xu, G. Feng, Y. Ren, and X. Zhang, “On Cloud Storage Optimization of Blockchain With a Clustering-Based Genetic Algorithm,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8547–8558, 2020.
    Crossref
  2. V. Kumar, A. Bajaj, N. Singla, N. Singla, and A. Grover, “Assessment and Future Directions for Clustering Optimization in Cloud Computing,” IEEE Xplore, 2023 (accessed Jan. 2024).
    IEEE
  3. M. Wang and Q. Zhang, “Optimized data storage algorithm of IoT based on cloud computing in distributed system,” Computer Communications, vol. 157, pp. 124–131, Jan. 2020.
    Crossref
  4. V. Simic, B. Stojanovic, and M. Ivanovic, “Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach,” Future Generation Computer Systems, vol. 101, pp. 909–920, 2019.
    Crossref
  5. A.-R. Al-Ghuwairi et al., “Optimizing Clustering Approaches in Cloud Environments,” International journal of interactive mobile technologies, vol. 17, no. 19, pp. 70–94, 2023.
    Crossref
  6. M. Ashawa, O. Douglas, J. Osamor, and R. Jackie, “Improving cloud efficiency through optimized resource allocation technique for load balancing using LSTM machine learning algorithm,” Journal of Cloud Computing, vol. 11, no. 1, pp. 1–17, 2022.
    Crossref
  7. T. Yu, “Unbalanced Big Data-Compatible Cloud Storage Method Based on Redundancy Elimination Technology,” Scientific Programming, vol. 2022, pp. 1–10, 2022.
    Crossref
  8. A. Katal, S. Dahiya, and T. Choudhury, “Energy efficiency in cloud computing data centers: a survey on software technologies,” Cluster Computing, vol. 26, pp. 1845–1875, Jan. 2022.
    Crossref
  9. A. H. A. AL-Jumaili, R. C. Muniyandi, M. K. Hasan, J. K. S. Paw, and M. J. Singh, “Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations,” Sensors, vol. 23, no. 6, p. 2952, Jan. 2023.
    Crossref
  10. J. Yao and Y. Zheng, “Research on Performance Optimization of Virtualized Server Cluster Based on Cloud Computing,” 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE), 2022.
    Crossref
  11. G. Fragiadakis, V. Liagkou, E. Filiopoulou, D. Fragkakis, C. Michalakelis, and M. Nikolaidou, “Cloud services cost comparison: a clustering analysis framework,” Computing, vol. 105, pp. 2061–2088, Jan. 2023.
    Crossref
  12. D. Liu, “Research on Particle Swarm Optimization Clustering Algorithm for Big Data Based on Cloud Storage Environment,” 2021 4th International Conference on Information Systems and Computer Aided Education, pp. 1610–1613, 2021.
    Crossref
  13. Y. Zhang, “Application of nonlinear clustering optimization algorithm in web data mining of cloud computing,” Nonlinear Engineering, vol. 12, no. 1, pp. 1–8, 2023.
    Crossref
  14. Xu, M., Feng, G., Ren, Y. and Zhang, X., 2020. On cloud storage optimization of blockchain with a clustering-based genetic algorithm. IEEE Internet of Things Journal, 7(9), pp.8547-8558.
    Crossref
  15. Junaid, M., Sohail, A., Rais, R.N.B., Ahmed, A., Khalid, O., Khan, I.A., Hussain, S.S. and Ejaz, N., 2020. Modeling an optimized approach for load balancing in cloud. IEEE access, 8, pp.173208-173226.
    Crossref
  16. M. Liu, L. Pan, and S. Liu, “Cost Optimization for Cloud Storage from User Perspectives: Recent Advances, Taxonomy, and Survey,” ACM Computing Surveys, vol. 55, no. 13s, pp. 1–37, 2023.
    Crossref
  17. S. Jayaprakash, M. D. Nagarajan, R. P. de Prado, S. Subramanian, and P. B. Divakarachari, “A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning,” Energies, vol. 14, no. 17, p. 5322, Jan. 2021.
    Crossref
  18. V. Sharma and M. Bala, “An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique,” Egyptian Informatics Journal, vol. 21, no. 4, pp. 201–208, 2020.
    Crossref
  19. J. Ren and L. Liu, “A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering,” Journal of Electrical and Computer Engineering, vol. 2022, p. e1476661, 2022.
    Crossref
  20. Y. Song, H.-J. Kim, H.-J. Lee, and J.-W. Chang, “A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing,” Applied Sciences, vol. 14, no. 2, p. 835, 2024.
    Crossref
  21. B. Seth, S. Dalal, V. Jaglan, D. Le, S. Mohan, and G. Srivastava, “Integrating encryption techniques for secure data storage in the cloud,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 4, p. e4108, 2020.
    Crossref
  22. M. Ghobaei-Arani, “A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud-based systems,” Soft Computing, vol. 25, no. 5, pp. 3813–3830, 2020.
    Crossref
  23. A. Shahidinejad, M. Ghobaei-Arani, and M. Masdari, “Resource provisioning using workload clustering in cloud computing environment: a hybrid approach,” Cluster Computing, vol. 24, pp. 319–342, 2020.
    Crossref
  24. D. A. Shafiq, N. Z. Jhanjhi, and A. Abdullah, “Load balancing techniques in cloud computing environment: A review,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 3910–3933, 2021.
    Crossref
  25. A. Meenakshi, H. Sirmathi, and J. Anitha Ruth, “Cloud computing-based resource provisioning using k-means clustering and GWO prioritization,” Soft Computing, vol. 23, no. 21, pp. 10781–10791, Jan. 2019.
    Crossref

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