Database Query Node Scaling With Predictive Capacity Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database query processing systems face challenges in scaling virtualized resources effectively, leading to downtime and inefficient use of computing resources due to inadequate dynamic capacity adjustments and horizontal scaling limitations.
Innovation Solution
A centralized system manages virtualized nodes by analyzing historical data to predict future demand, allowing for dynamic horizontal scaling with minimal downtime by adjusting connection limits and implementing standby nodes to optimize resource use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If virtualized nodes are scaled horizontally by transferring virtual workloads, then computing resource capacity is increased, but service downtime occurs during the transfer process
Solution Approach 1:
The system performs preliminary actions by predicting future demand using historical data before scaling is actually needed. This allows the system to proactively add virtualized nodes and transfer workloads during low-demand periods, avoiding service disruption when scaling is most critical.
Solution Approach 2:
The system implements dynamic scaling by continuously monitoring service data and automatically adjusting the number of virtualized nodes based on real-time demand conditions. This dynamic approach allows workload transfer to occur gradually and adaptively, minimizing service downtime while increasing computing capacity.
2Productivity
If the number of virtualized nodes is increased to handle demand spikes, then service responsiveness is improved, but computing resource consumption increases
Solution Approach 1:
The system uses feedback mechanisms by continuously collecting and analyzing service data from virtualized nodes. This feedback loop enables the system to automatically adjust the number of active nodes based on actual demand, ensuring high responsiveness when needed while reducing resource consumption during low-demand periods.
Solution Approach 2:
The system changes operational parameters by dynamically adjusting the number of virtualized nodes based on predicted and actual demand conditions. This parameter adjustment allows the system to optimize the balance between service responsiveness and computing resource consumption according to varying workload conditions.
3Loss of energy
If virtualized nodes are terminated to reduce resource consumption, then computing resource efficiency is improved, but service disruption occurs during node termination
Solution Approach 1:
The system performs preliminary actions by predicting demand trends before terminating virtualized nodes. This allows the system to proactively transfer workloads from nodes scheduled for termination to remaining active nodes, ensuring service continuity while improving computing resource efficiency through targeted node retirement.
4Adaptability or versatility
If connection limits are adjusted dynamically, then resource allocation flexibility is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically adjusting connection limits based on predicted and actual demand without requiring manual intervention. This automated approach increases resource allocation flexibility while managing system complexity through algorithmic control rather than manual configuration.
Data Source
AI summary
A system and method for scaling management. A method includes determining at least one change in a number of virtualized nodes to be implemented based on service data related to servicing of requests by the virtualized nodes; adjusting capacities of at least one of the virtualized nodes based on the service data; and implementing the at least one change by modifying the number of virtualized nodes.