Dynamic Data Transfer Optimization Between Machine Clusters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data transfer methods between disparate database instances are inefficient, as they rely on generic information and do not optimize transfer operations in real-time, leading to suboptimal performance in terms of time, resource usage, and compliance with transfer constraints.
Innovation Solution
A method and apparatus for managing data transfer between source and destination machine clusters that access relevant information to determine and implement optimized data transfer operations, such as compression, reordering, partitioning, and encoding, to minimize transfer time and resource consumption while adhering to constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic information is used for data transfer operations, then the transfer process is simple, but the transfer time and resource usage are suboptimal
Solution Approach 1:
The patent implements dynamic data transfer operations that adapt in real-time based on current system conditions. The transfer operations are not static but dynamically adjusted based on monitored parameters such as data characteristics, network conditions, and resource availability, thereby improving transfer efficiency without requiring overly complex pre-planned configurations.
Solution Approach 2:
The system changes transfer parameters dynamically during the data transfer process. Based on accessed information about the data and system state, the patent modifies transfer parameters such as compression level, partitioning strategy, and encoding methods to optimize transfer performance for specific conditions, resolving the contradiction between simplicity and efficiency.
2Loss of time
If real-time optimization is implemented during data transfer, then transfer time and resource usage are reduced, but the system complexity increases
Solution Approach 1:
The patent performs preliminary actions by accessing relevant information about the data and system conditions before executing the transfer operation. This preliminary assessment allows the system to pre-determine optimal transfer strategies, reducing actual transfer time while avoiding the need for complex real-time adjustments during the transfer process itself.
Solution Approach 2:
The system implements feedback mechanisms that monitor transfer progress and system conditions, using this information to adjust transfer operations. The feedback loop enables real-time optimization by continuously comparing actual performance against targets and making adjustments, thereby reducing transfer time while managing complexity through systematic control.
3Use of energy by moving object
If data is transferred without optimization, then the process is straightforward, but resource consumption is high
Solution Approach 1:
The patent dynamically changes transfer parameters such as compression level, partitioning strategy, and encoding methods based on data characteristics and system conditions. These parameter adjustments optimize resource consumption by adapting the transfer approach to match the specific requirements of each data set and system state, reducing overall resource usage while maintaining operational simplicity.
Data Source
AI summary
In a method for managing transfer of data from a source machine cluster to a destination machine cluster, information relevant to the transfer of data from the source machine cluster to the destination machine cluster is accessed. In addition, a data transfer operation that substantially optimizes the transfer of the data based upon the accessed information is determined. Furthermore, the determined data transfer operation is implemented to transfer the data from the source machine cluster to the destination machine cluster.