Current Volume 9
Abstract
With the age of cloud-native applications, data-intensive operations in multi-cloud environments have become more complicated to manage. With growing enterprises, unforeseen spikes in workload can dramatically hit the performance of the database, resulting in latency, downtime, and inefficiencies in costs. Conventional workload management practices, which are largely based on static provisioning of resources or rule-based monitoring, fail to capture the dynamic aspects of contemporary workloads. To address this challenge, this study investigates the application of Artificial Intelligence (AI) for proactive workload prediction and performance optimization in distributed cloud database systems.The study investigates the application of machine learning (ML) methods to cloud-native databases for predicting patterns of workload trends from historical usage patterns, query logs, and resource utilization metrics. In particular, LSTM, Random Forest, and linear regression models were used to examine workload variations and predict future peaks. Predictions enable dynamic scaling of resources and routing of queries to maximize throughput and reduce latency.A test environment was established with a hybrid cloud configuration of distributed nodes and datasets replicating academic and commercial-level applications. System performance prior to and after the incorporation of AI-based predictions was contrasted in the study. The findings indicate a significant improvement in resource allocation precision, a 25% decrease in query response time, and a 30% reduction in peak-hour system downtime. Additionally, the AI models reached prediction accuracies above 85%, demonstrating their viability for application into database management systems.This work contributes to the new research area of intelligent database systems through a practicable approach to real-time forecasting of workloads in distributed systems. This work further emphasizes the significance of AI towards making cloud services more resilient, scalable, and cost-efficient. The research ends with an overview of future research areas and challenges in implementation, such as real-time integration of feedback, adaptive learning models, and AI-driven data sharding methodologies.
Keywords
Artificial Intelligence for Cloud Computing, Workload Forecasting, Distributed Databases, Machine Learning, Cloud Resource Optimization, Database Performance, LSTM, Real-Time Analytics, Cloud Scalability, Intelligent Data Systems
Citations
IRE Journals:
Prof. Dr. Parin Somani
"AI-Powered Workload Prediction in Distributed Cloud Databases" Iconic Research And Engineering Journals Volume 8 Issue 12 2025 Page 1749-1756
IEEE:
Prof. Dr. Parin Somani
"AI-Powered Workload Prediction in Distributed Cloud Databases" Iconic Research And Engineering Journals, 8(12)
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