Dynamic Cluster Configuration Management for Data Processing Efficiency
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Solution Overview
Problem
In data-intensive distributed applications, maintaining data processing efficiency across virtual nodes in computing environments is challenging, especially with changes in software configurations and hardware allocations.
Innovation Solution
A management system that monitors data processing efficiency information for clusters and transitions them from one configuration to another when efficiency criteria are met, identifying and implementing an optimized configuration to enhance data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If virtualization techniques are used to deploy multiple virtual nodes on a host, then resource utilization efficiency is improved, but maintaining data processing efficiency becomes more difficult
Solution Approach 1:
The patent implements dynamic configuration management that automatically adjusts virtual node configurations based on monitored performance metrics. The system transitions clusters between different configurations (first configuration to second configuration) when efficiency criteria are met, enabling the system to adapt to changing conditions and maintain optimal data processing efficiency while utilizing virtualization benefits
Solution Approach 2:
The patent employs a feedback mechanism where the management system continuously monitors data processing efficiency information for virtual node clusters. Based on this feedback, the system determines when to transition configurations and identifies optimized configurations, creating a closed-loop control system that maintains reliability despite virtualization complexity
2Adaptability or versatility
If software configurations and hardware allocations are changed to virtual nodes, then adaptability is improved, but data processing efficiency becomes harder to maintain
Solution Approach 1:
The system dynamically adjusts configurations based on monitored efficiency metrics. When data processing efficiency information meets specific criteria, the management system automatically transitions the cluster from a first configuration to a second configuration, ensuring that adaptability improvements do not compromise processing efficiency
Solution Approach 2:
The patent changes configuration parameters (software configurations and hardware allocations) of virtual nodes based on monitored performance data. The system identifies optimized configurations that improve adaptability while maintaining data processing efficiency through systematic parameter adjustment rather than arbitrary changes
Data Source
AI summary
Described herein are systems, methods, and software to enhance the management and deployment of data processing clusters in a computing environment. In one example, a management system may monitor data processing efficiency information for a cluster and determine when the efficiency meets efficiency criteria. When the efficiency criteria are met, the management system may identify a new configuration for the cluster and initiate an operation to implement the new configuration for the cluster.