Predictive Analytics for Cloud Database Performance Optimization and High-Availability Systems
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-117Keywords:
Predictive Analytics, Cloud Databases, Performance Optimization, High Availability, Machine Learning, Fault Prediction, Resource Scaling, Time-Series Forecasting, Proactive Monitoring, Query Optimization, Workload Prediction, Sla Management, Failover Automation, Anomaly Detection, Cloud ComputingAbstract
Cloud databases are an indispensable part of modern applications and they are required to maintain high performance and availability levels even in the face of highly variable workloads, resource contention and infrastructure failures. Conventional reactive monitoring and scaling methods usually only kick in after performance has been degraded which results in SLA violations and inefficient use of resources. Predictive analytics comes to the rescue by investigating both historical and current database and system indicators like for instance CPU utilization, memory usage, I/O latency, query execution times, and failure patterns to predict performance bottlenecks and availability problems before users are affected. Machine learning and time-series forecasting enable the creation of prediction models which in turn fuel proactive steps like dynamic resource provisioning, intelligent query optimization, replica management, and automated failover. This paper describes a predictive analytics–based framework for performance tuning and high-availability management of cloud databases, which is demonstrated on a cloud-hosted relational database. The outcome demonstrates that query latency can be lowered, resource utilization can be better, and failure recovery can be speeded up in comparison with reactive approaches which means predictive analytics is a very effective tool for enhancing both performance and resilience in cloud database systems.
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