Heterogeneous Cloud Database Configuration Optimization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing technologies struggle to efficiently tune cloud-based NoSQL databases for performance under cost constraints, especially in dynamic workloads with changing VM and DBMS configuration dependencies.

Innovation Solution

A system and method for heterogeneous configuration optimization and deployment that forecasts workloads, generates reconfiguration plans, and optimizes both database and cloud configurations to maximize performance per dollar, while minimizing reconfiguration performance hits.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If frequent reconfiguration is performed to adapt to dynamic workloads, then workload adaptability is improved, but reconfiguration performance hits and downtime increase

Engineering Contradiction:

Improveworkload adaptabilityVSAvoidreconfiguration performance hits

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary actions by forecasting future workloads using machine learning models before reconfiguration is needed. This allows the system to prepare and schedule reconfigurations in advance, reducing the impact of reconfiguration downtime and enabling smoother transitions to optimal configurations for anticipated workload changes.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements dynamic reconfiguration strategies where configuration decisions are made based on predicted future workloads rather than static historical patterns. This dynamic approach allows the system to proactively adjust configurations before workload changes occur, reducing the frequency and impact of disruptive reconfigurations.

Inventive Principle:

Principle #15Dynamics

2Productivity

If comprehensive configuration optimization is performed, then performance per dollar is improved, but system complexity and optimization time increase

Engineering Contradiction:

Improveperformance per dollarVSAvoidconfiguration optimization complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the configuration optimization problem into multiple manageable components: VM-level configurations, database-level configurations, and their interactions. This segmentation allows the complex optimization to be broken down into smaller sub-problems that can be solved independently and combined, reducing overall system complexity while maintaining comprehensive optimization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary characterization of workload patterns and configuration dependencies before optimization begins. By pre-computing workload forecasts and configuration interaction models, the system reduces the complexity of real-time optimization decisions and enables more efficient comprehensive configuration tuning.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If VM and database configurations are jointly optimized, then overall performance is improved, but the search space and optimization difficulty increase

Engineering Contradiction:

Improveoverall performanceVSAvoidsearch space size

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system merges VM-level and database-level configuration optimizations into a unified joint optimization framework. By combining these previously separate optimization problems, the system captures the interactions between different configuration layers and achieves superior overall performance that would not be possible through independent optimization of each layer.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system performs preliminary analysis to characterize the relationships and dependencies between VM configurations and database configurations. This pre-computed knowledge of configuration interactions reduces the effective search space by eliminating incompatible or suboptimal combinations, making joint optimization feasible despite the large number of configuration parameters.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250130845A1System and methods for heterogeneous configuration optimization for distributed servers in the cloud

Publication Date: 2025.04.24 PURDUE RES FOUND

AI summary

A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.