Database Upgrade Optimization Using Fuzzy Logic Ranking
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Solution Overview
Problem
Existing system upgrade techniques often result in undesirable downtime due to the unavailability of databases during the upgrade process, exacerbated by limited resources, making it challenging to maintain system availability during upgrades.
Innovation Solution
A computer system utilizing fuzzy logic to rank and select databases for upgrade during system uptime based on resource limitations, employing a shadow system to process upgrades while the original system remains operational, thereby minimizing downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all databases are upgraded simultaneously using traditional techniques, then the upgrade process can be completed, but the entire system becomes unavailable causing excessive downtime
Solution Approach 1:
The upgrade process is segmented into multiple phases: identification of databases for upgrade, calculation of parameter values, fuzzy logic-based ranking, and selective upgrading. This segmentation allows the system to upgrade databases in controlled batches rather than all at once, maintaining availability while completing upgrades.
Solution Approach 2:
The system performs preliminary actions by identifying databases for upgrade and calculating their parameter values before the actual upgrade begins. The fuzzy logic module pre-ranks databases based on multiple parameters, allowing the system to prepare upgrade sequences that minimize downtime while maintaining operational availability.
2Productivity
If upgrade resources are increased to speed up the upgrade process, then more databases can be upgraded during uptime, but resource availability and cost increase
Solution Approach 1:
The system changes parameters by calculating multiple parameter values for each database (size, complexity, priority, etc.) and using fuzzy logic to dynamically adjust the ranking and selection criteria. This allows optimal utilization of limited resources by prioritizing databases that provide the best upgrade value, rather than simply increasing resource quantity.
Solution Approach 2:
The fuzzy logic module automatically ranks and selects databases for upgrade based on calculated parameters and resource constraints, without requiring manual intervention. The system self-optimizes the upgrade sequence to maximize productivity within available resource limits.
3Manufacturing precision
If multiple parameters are considered for database selection, then upgrade priority can be optimized, but the complexity of the selection process increases
Solution Approach 1:
The fuzzy logic module acts as an intermediary between multiple input parameters and the final database selection. It processes multiple parameters (database size, priority, resource requirements, etc.) through fuzzy logic rules to produce a single relative ranking, simplifying the decision-making process while maintaining high precision in upgrade priority determination.
Data Source
AI summary
In one general aspect, a computer system can include instructions stored on a non-transitory computer-readable storage medium. The computer system can include a upgrade portion identifier configured to identify a plurality of databases of an original system for upgrade, and a parameter module configured to calculate a plurality of parameter values representing aspects of the plurality of databases. The computer system can also include a fuzzy logic module configured to calculate, using fuzzy logic, a relative ranking of each database from the plurality of databases based on the plurality of parameter values, and a selection module configured to select at least a portion of the plurality of databases for upgrade during uptime of the original system based on a limitation of an upgrade resource and the relative ranking of each database from the plurality of databases.