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Intelligent load balancing and concurrency control in cloud-based distributed databases: A machine learning approach
Department of Industrial Engineering, Texas A and M University, Kingsville, Texas.
Research Article
International Journal of Science and Research Archive, 2023, 09(01), 847-854.
Article DOI: 10.30574/ijsra.2023.9.1.0350
Publication history:
Received on 02 April 2023; revised on 15 May 2023; accepted on 18 May 2023
Abstract:
Cloud-based distributed databases are critical for scalable modern applications, yet they struggle with uneven resource utilization and transaction conflicts. This paper introduces a machine learning (ML)driven framework combining reinforcement learning (RL) for dynamic load balancing and a hybrid concurrency control protocol. The RL agent optimizes query distribution by analyzing real-time node metrics, while the concurrency controller adaptively switches between optimistic and pessimistic strategies based on conflict predictions. Evaluations on AWS EC2 using the YCSB benchmark demonstrate a 30% improvement in throughput, 25% reduction in latency, and a 47% decrease in abort rates compared to traditional methods. The results validate the efficacy of AI-driven solutions in enhancing cloud database performance and scalability.
Keywords:
Cloud-based distributed databases; Reinforcement learning (RL); Dynamic load balancing; Hybrid concurrency control; Conflict prediction; Throughput improvement; Latency reduction; AIdriven solutions
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
