Cloud Data Migration Path Selection
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Solution Overview
Problem
Current data migration technologies in cloud storage lack efficiency in determining optimal paths for data transfer, leading to increased costs and processing complexities due to queue accumulation and varying performance across different storage clusters and clouds.
Innovation Solution
A computer-implemented method that determines possible paths for data storage changes, calculates weighted transfer times, and applies user-defined weights to prioritize or avoid specific clusters or clouds, optimizing data migration by selecting the path with the minimum weighted transfer time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional queueing mechanism is used for data migration, then data transfer is performed in order, but transfer time increases due to queue accumulation
Solution Approach 1:
The system dynamically evaluates multiple migration paths and adjusts path selection based on real-time conditions. Instead of following a static queueing mechanism, the system can switch between different paths (direct migration, premigration + recall, or alternative routes) to optimize transfer time while maintaining data integrity through weighted decision-making that considers current system state
Solution Approach 2:
The system introduces an intermediary decision-making layer that evaluates multiple paths and selects the optimal route for each data migration request. This intermediary mechanism (the path selection system with weighted evaluation) mediates between the requirement for ordered processing and the need for fast transfer by routing requests through different paths based on current conditions
2Adaptability or versatility
If multiple storage clusters and clouds are used for data migration, then flexibility and options increase, but path selection complexity increases
Solution Approach 1:
The system uses parameter changes (weights) to simplify the complexity of multiple path options. By assigning weights to different paths based on criteria such as transfer speed, cost, and current load, the system transforms a complex multi-parameter decision problem into a simplified weighted comparison that maintains adaptability across multiple storage clusters and clouds
Solution Approach 2:
The system provides self-service through automated path selection that evaluates and chooses the optimal migration path without requiring manual intervention. The weighted evaluation mechanism automatically adapts to changing conditions and selects the best path, reducing the operational complexity of managing multiple storage destinations
3Measurement precision
If all possible paths are evaluated with detailed weights, then optimal path selection improves, but processing complexity increases
Solution Approach 1:
The system maintains measurement precision by using weighted parameters to evaluate paths, but simplifies processing complexity through a standardized weighted scoring framework. The weights transform complex quality attributes (speed, cost, reliability) into comparable numerical values that can be efficiently processed and compared across multiple paths
Data Source
AI summary
A data storage change is received for a piece of data, wherein the data storage change is from a first location to a second location. Two or more possible paths to perform the data storage change are determined. A plurality of weights for each path of the two or more paths is determined. A weighted transfer time for each path of the two or more paths is determined.