Autonomous Database Provisioning Across Datacenters
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Solution Overview
Problem
Current virtual computing systems face limitations in efficiently managing and provisioning databases across multiple datacenters and environments, leading to suboptimal performance and resource utilization due to manual cluster selection and lack of autonomous decision-making for database placement.
Innovation Solution
A method and system where a processor in a database management system autonomously selects a datacenter and cluster based on predefined rules to provision databases, determining network locations and optimizing database placement for improved performance and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual cluster selection is used for database provisioning, then system complexity is reduced, but resource utilization and performance efficiency deteriorate
Solution Approach 1:
The database management system autonomously selects datacenters and clusters for database provisioning without requiring manual intervention. The system evaluates multiple criteria including performance metrics, resource availability, and operational costs to automatically determine optimal deployment locations, thereby improving resource utilization while maintaining manageable system complexity through automated decision-making
Solution Approach 2:
The system dynamically changes provisioning parameters by considering multiple factors such as performance metrics, resource availability, and operational costs. These parameter evaluations enable the system to adaptively select optimal datacenters and clusters, resolving the contradiction between system complexity and resource utilization efficiency
2Extent of automation
If manual cluster selection is used for database provisioning, then automation extent is reduced, but system complexity is lowered
Solution Approach 1:
The database management system performs autonomous selection of datacenters and clusters by automatically evaluating performance metrics, resource availability, and operational costs. This self-service capability increases the extent of automation while the modular architecture and standardized evaluation criteria keep system complexity manageable
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor performance metrics and resource availability across multiple datacenters. This feedback loop enables autonomous decision-making by providing real-time data for evaluating provisioning options, thereby increasing automation extent while maintaining controlled system complexity through systematic information processing
3Reliability
If databases are provisioned across multiple datacenters, then availability is improved, but network latency and operational costs increase
Solution Approach 1:
The system applies local quality by provisioning database copies or partitions in datacenters that are geographically or network-wise closest to the users or applications requiring access. This approach maintains high availability through multi-datacenter deployment while minimizing network latency by optimizing the location of database instances based on access patterns and network conditions
Solution Approach 2:
The system dynamically changes provisioning parameters by evaluating performance metrics including network latency when selecting datacenters for database deployment. This enables optimization of the balance between availability and latency by adjusting where databases are provisioned based on real-time or historical performance data
4Productivity
If autonomous selection based on multiple criteria is used, then resource utilization is improved, but computational complexity increases
Solution Approach 1:
The system segments the autonomous selection process into distinct evaluation stages, assessing different criteria such as performance metrics, resource availability, and operational costs separately. This segmentation improves resource utilization through comprehensive multi-criteria evaluation while managing computational complexity by breaking down the decision-making process into manageable, modular components
Data Source
AI summary
A database management system receives a user request for provisioning a database, autonomously selects a datacenter for provisioning the database based at least on a first pre-defined rule, autonomously selects at least one cluster in the datacenter based at least on a second pre-defined rule, determines a network location of each of the at least one cluster, and provisions the database on each of the at least one cluster using the network location of each of the at least one cluster.