Dynamic Cost Profile Evolution for Heterogeneous Database Workloads

The patent transforms static cost profiles into dynamic cost profiles that automatically adapt to changing workload characteristics and resource configurations. The system continuously learns from executed queries and updates cost profiles in real-time, enabling the query optimizer to select execution plans that are optimal for the current system state rather than relying on predetermined static values.

The system implements self-service through automatic cost profile learning and update mechanisms. The query optimizer autonomously monitors workload patterns, resource utilization, and execution performance, then automatically adjusts cost profiles without requiring manual intervention or reconfiguration by system administrators.

The system implements feedback loops where execution performance metrics are continuously collected and fed back into the cost profile learning mechanism. The query optimizer monitors actual execution costs versus estimated costs, and uses this feedback to refine cost profiles for different workload types and resource configurations, progressively improving adaptability.

The patent dynamically adjusts cost profile parameters based on learned workload characteristics and resource performance. Instead of using fixed cost values, the system modifies cost parameters in real-time to reflect actual system behavior, enabling effective adaptation across heterogeneous workloads while maintaining operational simplicity through automated parameter tuning.

The system performs preliminary learning and cost profile generation for various resource configurations before actual queries are executed. By pre-characterizing different cloud resource states and their performance implications, the query optimizer can reliably select appropriate execution plans even when resources dynamically change, as the cost profiles already encode knowledge about resource behavior.

The patent replaces traditional mechanical configuration approaches (manual cost profile setting for each resource state) with an intelligent learning system that automatically adapts to resource changes. The machine learning-based cost profile generation substitutes rigid mechanical configuration with flexible, data-driven adaptation, maintaining reliability through learned patterns rather than static rules.