Adaptive Data Recovery Scheme Modification for Network Performance
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Solution Overview
Problem
Conventional data recovery schemes are inefficient as they fail to account for changing operation requirements and performance characteristics of data centers, leading to static and suboptimal data relocation strategies.
Innovation Solution
A system that optimizes data recovery schemes by using a data management component to modify the scheme based on performance data and recovery requirements, employing machine learning models to determine optimal data relocation within a network of data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional static data recovery schemes are used, then implementation simplicity is maintained, but data relocation efficiency deteriorates due to inability to adapt to changing performance characteristics
Solution Approach 1:
The patent implements dynamic data recovery schemes where secondary data centers are not predetermined but selected in real-time based on current performance characteristics. The system continuously monitors data center performance metrics and adapts the recovery scheme dynamically, transforming the static replication model into a dynamic one that responds to changing conditions, thereby improving relocation efficiency without requiring overly complex manual configuration
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring performance characteristics of data centers and using this information to adjust data relocation decisions. The feedback loop collects data on performance metrics, analyzes them through machine learning models, and uses the results to optimize the recovery scheme continuously, resolving the contradiction between simplicity and efficiency by automating the adaptation process
2Adaptability or versatility
If static secondary data centers are predetermined, then scheme establishment is simplified, but adaptability to changing operation requirements deteriorates
Solution Approach 1:
The system employs machine learning models that enable the data recovery scheme to self-optimize without manual intervention. The models automatically analyze performance data, identify optimal secondary data centers, and adjust the recovery scheme autonomously, providing adaptability to changing requirements while maintaining operational simplicity through automation rather than complex manual processes
Solution Approach 2:
The patent changes the fundamental parameter of secondary data center selection from static predetermined values to dynamic selections based on real-time performance characteristics. By using machine learning models to process performance data and determine optimal targets, the system achieves high adaptability while the automated nature of the process prevents excessive complexity in scheme management
3Productivity
If machine learning models are used to optimize data recovery, then performance optimization is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between raw performance data and recovery decision-making. These models act as mediators that automatically process complex performance metrics and translate them into optimized relocation decisions, improving recovery performance while shielding the overall system from the complexity of raw data analysis through automated intermediate processing layers
Data Source
AI summary
Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.