Unraveling Multi-tenancy Issues in Aurora MySQL using AI/ML | Let's Talk About Data

Managing performance and optimizing resources in multi-tenant Aurora MySQL environments presents unique challenges. This presentation introduces an AI/ML-powered monitoring pipeline for multi-tenant Aurora MySQL environments, addressing performance and resource optimization challenges.

The solution utilizes Amazon SageMaker for Buffer Pool Analysis by leveraging advanced statistical techniques and machine learning algorithms, the system identifies schema-level workload patterns and detects anomalies. Using Amazon Bedrock, we generate specific actionable recommendations.
The presentation includes technical deep-dives into integration architecture, outlier detection, and data transformation methods, demonstrating how this approach surpasses standard monitoring capabilities. Using a Real-world example, we illustrate how AI/ML analysis of InnoDB buffer pool data identifies noisy neighbors, resource contentions, and produces targeted optimization strategies, revolutionizing multi-tenant database management with AI-driven insights.

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