Enhanced Naked Mole-Rat Optimization Based Data Replication in Cloud Computing

Authors

  • Navneet Vishnoi-I Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Vikram Singh Assistant professor, School of Computer Science & System, JAIPUR NAITONAL UNIVERSITY, JAIPUR, India
  • Sachin Sharma Assistant Professor, School of Engineering and Computer, Dev Bhoomi Uttarakhand University, Uttarakhand, India
  • Pawan Kumar Assistant Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India

Keywords:

Data replication, cloud computing, data centers, Enhanced Naked Mole-Rat Optimization

Abstract

Over the last several years, there has been a significant increase in the study of cloud computing, including replication systems and their uses. As the number of replicas grows and they distribute out, the cost of maintaining the system data availability, performance, and consistency rises as well. This research optimizes the choice and placement of data replication on the cloud by an intelligence optimization algorithm with several goals. In this we proposed Enhanced Naked Mole-Rat Optimization (ENMRO). The most common data replication approach is utilized to identify the best-chosen data replicate first. Then it is used to determine the optimal location for a data copy based on the shortest space, the quantity of data transfers, and the accessibility of data replication. The recommended technique has been put through a simulation utilizing cloudsim. The Cloud is intended to mimic many kinds of data centers (DCs) with different architectures. Every DC is made up of a host that hosts a collection of virtual machines (VMs) that provide replications of accessible data blocks. Several well-known techniques, including Replica Selection and Placement (RSP), Genetic Algorithm (GA), Dynamic Cost aware Re-replication and Re-balancing Strategy (DCR2S), Dynamic Replica Selection Ant Colony Optimization (DRSACO) were used to compare the accomplishment of the suggested approach. The investigational findings demonstrate that ENMRO replicates data more effectively than comparative algorithms. In comparison to other algorithms, it also delivers greater data availability, reduced costs, and lower bandwidth usage.

Downloads

Download data is not yet available.

References

Salem, R., Salam, M.A., Abdelkader, H. and Mohamed, A.A., 2019. An artificial bee colony algorithm for data replication optimization in cloud environments. IEEE Access, 8, pp.51841-51852.

Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A. and Buyya, R., 2011. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), pp.23-50.

Rajeshirke, N., Sawant, R., Sawant, S. and Shaikh, H., 2017. Load balancing in cloud computing. Int J Recent Trends Eng Res, 3(3), pp.260-267.

Haricha, K., Khiat, A., Issaoui, Y., Bahnasse, A. and Ouajji, H., 2023. Recent technological progress to empower Smart Manufacturing: Review and Potential Guidelines. IEEE Access.

Ahn, H.Y., Lee, K.H. and Lee, Y.J., 2016. Dynamic erasure coding decision for modern block-oriented distributed storage systems. The Journal of Supercomputing, 72, pp.1312-1341.

Maheshwari, N., Nanduri, R. and Varma, V., 2012. Dynamic energy efficient data placement and cluster reconfiguration algorithm for MapReduce framework. Future Generation Computer Systems, 28(1), pp.119-127.

Milani, B.A. and Navimipour, N.J., 2016. A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. Journal of Network and Computer Applications, 64, pp.229-238.

Boru, D., Kliazovich, D., Granelli, F., Bouvry, P. and Zomaya, A.Y., 2015. Energy-efficient data replication in cloud computing data centers. Cluster computing, 18, pp.385-402.

Sun, D.W., Chang, G.R., Gao, S., Jin, L.Z. and Wang, X.W., 2012. Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. Journal of computer science and Technology, 27(2), pp.256-272.

Shakarami, A., Ghobaei-Arani, M., Shahidinejad, A., Masdari, M. and Shakarami, H., 2021. Data replication schemes in cloud computing: a survey. Cluster Computing, 24, pp.2545-2579.

Boru, D., Kliazovich, D., Granelli, F., Bouvry, P. and Zomaya, A.Y., 2015, June. Models for efficient data replication in cloud computing data centers. In 2015 IEEE international conference on Communications (ICC) (pp. 6056-6061). IEEE.

Mansouri, N. and Javidi, M.M., 2020. A review of data replication based on a meta-heuristics approach in cloud computing and data grid. Soft computing, 24, pp.14503-14530.

Mansouri, N., Javidi, M.M. and Mohammad Hasani Zade, B., 2021. A CSO-based approach for secure data replication in the cloud computing environment. The Journal of Supercomputing, 77, pp.5882-5933.

Milani, B.A. and Navimipour, N.J., 2017. A systematic literature review of the data replication techniques in the cloud environments. Big Data Research, 10, pp.1-7.

Hussein, M.K. and Mousa, M.H., 2012. A lightweight data replication for cloud data centers environment. International Journal of Engineering and Innovative Technology, 1(6), pp.169-175.

Lie, W., Jiang, B. and Zhao, W., 2020. Obstetric imaging diagnostic platform based on cloud computing technology under the background of smart medical big data and deep learning. IEEE Access, 8, pp.78265-78278.

Tos, Uras, Riad Mokadem, Abdelkader Hameurlain, Tolga Ayav, and Sebnem Bora. "A performance and profit-oriented data replication strategy for cloud systems." In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 780-787. IEEE, 2016.

Tabet, K., Mokadem, R., Laouar, M.R. and Eom, S., 2017. Data replication in cloud systems: a survey. International Journal of Information Systems and Social Change (IJISSC), 8(3), pp.17-33.

M. Bsoul, A. Al-Khasawneh, E. Abdallah, and Y. fKilani, “Enhanced Fast Spread Replication strategy for Data Grid”, Journal of Network and Computer Applications, Vol. 34, pp. 575–580, 2011.

N. Gill and S. Singh, “A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers”, Future Generation Computer Systems, Vol. 65, pp. 10– 32, 2016.

Shao, Z.L., Huang, C. and Li, H., 2021. Replica selection and placement techniques on the IoT and edge computing: a deep study. Wireless Networks, 27(7), pp.5039-5055.

L. Wang, J. Luo, J. Shen, and F. Dong, "Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment", In Proc. of International Conf. on Big Data, Santa Clara, USA, Vol. 1, pp. 247-254,2013.

N. Navimipour and B. Milani, “Replica selection in the cloud environments using an ant colony algorithm”, In: Proc. of International Conf. on Control Engineering and Communication Technology, Moscow, Russia, pp. 1-9, 2016.

L. Cui, J. Zhang, L. Yue, Y. Shi, H. Li, and D. Yuan, “A Genetic Algorithm Based Data Replica Placement Strategy for Scientific Applications in Clouds”, IEEE Transaction on Services Computing, Vol. 11, pp. 727-739, 2018.

Jadhav, S. B. ., & Kodavade, D. V. . (2023). Enhancing Flight Delay Prediction through Feature Engineering in Machine Learning Classifiers: A Real Time Data Streams Case Study. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 212–218. https://doi.org/10.17762/ijritcc.v11i2s.6064

Moore, B., Clark, R., Martinez, J., Rodriguez, A., & Rodriguez, L. Anomaly Detection in Internet of Things (IoT) Data Streams. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/151

Downloads

Published

04.11.2023

How to Cite

Vishnoi-I, N. ., Singh, V. ., Sharma, S. ., & Kumar, P. . (2023). Enhanced Naked Mole-Rat Optimization Based Data Replication in Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 355–362. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3714

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

License

Copyright (c) 2023 Navneet Vishnoi-I, Vikram Singh, Sachin Sharma, Pawan Kumar

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.