Optimizing Database Replication Strategies through Machine Learning for Enhanced Fault Tolerance in Cloud-Based Environments
Main Article Content
Abstract
In the modern, even more and more virtual world, database replication is very useful for data access and protection. However, reduced replications pose issues such as latency, data synchronization, and failed recovery, which are issues with usual replication methodologies. This article analyses the usage of machine learning methods to increase database replication and improve fault tolerance in the cloud. Using advanced workload prediction, anomaly detection, and replication techniques, it is possible to be proactive by predicting when workloads will appear and making systems more robust when they don't need to be heavily used. This paper describes the types of database replication and how the industry views machine learning, proven use cases, and practical recommendations. Last, it describes future trends and issues in enhancing database replication using machine learning and how future technological advances demand the system's commensurate enhancements.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Alam, M. M., & Hassan, A. (2023). "A Machine Learning Approach for Optimiz Database Replication Strategies in Cloud Environments." Journal of Cloud Computing: Advances, Systems and Applications, 12(1), 1-16.
Khan, M. A., & Liu, Y. (2023). "Fault Tolerance" in Distributed Databases: A Machine Learning Perspective." IEEE Transaction" on Dependable and Secure Computing, 20(2), 560-573.
Patel, S. K., & Desai, S. P. (2023). "Dynamic Load Balancing in Cloud Databases Using Machine Learning Techniques." International Journal of Cloud Computing and Services Science, 12(2), 112-124.
Gonzalez, J. C., & Smith, R. (2022). "Anomaly Detection for Cloud Database Systems Using Machine Learning: Techniques and" Applications." ACM Computing Surveys, 55(6), Article 120.
Choudhary, A., & "Gupta, R. (2022). "Optimizing Database Replication Strategies Using Reinforcement "Learning." Journal of Systems Architecture, 128, 101750.
Zhou, Y., & Liu, H. (2022). "Predictive Analytics for Database Performance Optimization in Cloud Environments" Future Generation Computer Systems, 129, 196-207.
Bansal, R., & Prakash, A. (2021)."Data Consistency in Cloud-Based Database Systems: A Machine Learning Approach" Journal of Computer and System Sciences, 112, 148-159.
Singh, V., & Kuma", R. (2021). "A Comprehensive Survey on Fault Tolerance Mechanisms in Distributed Databases" ACM Computing Surveys, 54(4), Article 80.
Ahmed, S., & Javed, M. (20 "1). "Machine Learning for Database Management: A Review of Current Trends and Future Directions." Information Systems, 100, 101817.
Kumar, A., & Choudhury, S. (2020). "Cloud Database Performance Optimization: A Machine Learning Approach." IEEE Access, 8, 98765-98779.
Elemam, S. M. (2018). Pragmatic Competence and the Challenge of Speech Expression and Precision (Master's thesis, University of Dayton).
Kothandapani, H. P. (2020). Application of machine learning for predicting us bank deposit growth: A univariate and multivariate analysis of temporal dependencies and macroeconomic interrelationships. Journal of Empirical Social Science Studies, 4(1), 1-20.
Kothandapani, H. P. (2019). Drivers and barriers of adopting interactive dashboard reporting in the finance sector: an empirical investigation. Reviews of Contemporary Business Analytics, 2(1), 45-70.
Kothandapani, H. P. (2021). A benchmarking and comparative analysis of python libraries for data cleaning: Evaluating accuracy, processing efficiency, and usability across diverse datasets. Eigenpub Review of Science and Technology, 5(1), 16-33.
Rahman, M.A., Butcher, C. & Chen, Z. Void evolution and coalescence in porous ductile materials in simple shear. Int J Fracture, 177, 129–139 (2012). https://doi.org/10.1007/s10704-012-9759-2
Rahman, M. A. (2012). Influence of simple shear and void clustering on void coalescence. University of New Brunswick, NB, Canada. https://unbscholar.lib.unb.ca/items/659cc6b8-bee6-4c20-a801-1d854e67ec48
Alam, H., & De, A., & Mishra, L. N. (2015). Spring, Hibernate, Data Modeling, REST and TDD: Agile Java design and development (Vol. 1)
Ahuja, Ashutosh. (2024). OPTIMIZING PREDICTIVE MAINTENANCE WITH MACHINE LEARNING AND IOT: A BUSINESS STRATEGY FOR REDUCING DOWNTIME AND OPERATIONAL COSTS. 10.13140/RG.2.2.15574.46400.
Al Bashar, M., Taher, A., & Johura, F. T. (2019). QUALITY CONTROL AND PROCESS IMPROVEMENT IN MODERN PAINT INDUSTRY.
Al Bashar, M., Taher, M. A., Islam, M. K., & Ahmed, H. (2024). The Impact Of Advanced Robotics And Automation On Supply Chain Efficiency In Industrial Manufacturing: A Comparative Analysis Between The Us And Bangladesh. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(03), 28-41.
Ahmed, H., Al Bashar, M., Taher, M. A., & Rahman, M. A. (2024). Innovative Approaches To Sustainable Supply Chain Management In The Manufacturing Industry: A Systematic Literature Review. Global Mainstream Journal of Innovation, Engineering & Emerging Technology, 3(02), 01-13.
Vaithianathan, M. (2024). Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems. International Journal of Computer Trends and Technology, 72(4), 145-152.
Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2023). Comparative Study of FPGA and GPU for High-Performance Computing and AI. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1), 37-46.
Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Integrating AI and Machine Learning with UVM in Semiconductor Design. ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume, 2, 37-51.
Zhu, Y. (2023). Beyond Labels: A Comprehensive Review of Self-Supervised Learning and
Intrinsic Data Properties. Journal of Science & Technology, 4(4), 65-84.
Y. Pei, Y. Liu and N. Ling, "MobileViT-GAN: A Generative Model for Low Bitrate Image Coding," 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP), Jeju, Korea, Republic of, 2023, pp. 1-5, doi: 10.1109/VCIP59821.2023.10402793.
Y. Pei, Y. Liu, N. Ling, Y. Ren and L. Liu, "An End-to-End Deep Generative Network for Low Bitrate Image Coding," 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA, 2023, pp. 1-5, doi: 10.1109/ISCAS46773.2023.10182028.