Intelligent Data Replication Strategies: Using AI to Enhance Fault Tolerance and Performance in Multi-Node Database Systems

Authors

  • Prathyusha Nama
  • Purushotham Reddy
  • Guru Prasad Selvarajan

Keywords:

AI-enhanced replication, multi-node database systems, fault tolerance, performance optimization, adaptive replication, machine learning, edge computing

Abstract

Advancements in managing data have pushed the development of new concepts needed to appropriately advance organizations' multi-node database systems. The present research paper concentrates on a review of artificial intelligence for data replication tasks and demonstrates AI treatment of real-time data and its contribution to augment replication techniques. The focus areas include adaptive replication, fault predictability and control, and intelligent load distribution to help increase the system's flexibility to meet users' expectations. Further support for these strategies is evident from Netflix and Facebook. Besides, one can never run out of fashion to explore the market, such as edge computing, blockchain, and better algorithms to replicate data. Nonetheless, significant implementation issues, data use, and resource-making must be solved to get the full potential of these innovations. Consequently, this Article offers some vision of the future of replication with the help of AI and how it may influence the very concept of data management within the context of a rapidly emerging data-oriented world.

References

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Published

31-12-2023

How to Cite

Prathyusha Nama, Purushotham Reddy, & Guru Prasad Selvarajan. (2023). Intelligent Data Replication Strategies: Using AI to Enhance Fault Tolerance and Performance in Multi-Node Database Systems. Well Testing Journal, 32, 110–122. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/111

Issue

Vol. 32 (2023): Special Issue

Section

Research Articles

License

Copyright (c) 2023 Well Testing Journal

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