Federated Cloud Repository Data Fragmentation
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Solution Overview
Problem
Migration repositories in cloud computing often fail to store data effectively, leading to stale, duplicated, or incomplete repository data that does not accurately reflect the current cloud resource topology, hindering efficient migration processes.
Innovation Solution
The federation of repository data into multiple federated data stores as data fragments allows for more effective updating and reuse of migration data, using declarative language and semantic technologies to dynamically query machine-discoverable metadata, ensuring data remains current and improves the knowledge base for generating models and mappings during migrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If repository data is stored in a centralized migration repository, then data storage is simplified, but the data becomes stale, duplicated, or incomplete and does not accurately reflect current cloud resource topology
Solution Approach 1:
The patent segments the centralized repository into multiple federated data stores distributed across different clouds and networks. Each data store holds fragments of migration data locally, eliminating the single point of stale data while maintaining data accuracy through distributed updates.
Solution Approach 2:
The patent transitions from a single-dimensional centralized repository to a multi-dimensional federated architecture where data exists across multiple dimensions (different clouds, networks, and locations). This dimensional expansion allows simultaneous data storage and continuous synchronization to prevent staleness.
2Measurement precision
If migration data is updated frequently in a centralized repository, then data accuracy improves, but data synchronization and updating efficiency deteriorates
Solution Approach 1:
By segmenting the repository into federated data stores, each location can update its local fragments independently without triggering system-wide synchronization. This maintains data accuracy through local updates while preserving productivity by eliminating centralized coordination overhead.
Solution Approach 2:
The patent implements feedback mechanisms where federated data stores continuously query and update their local fragments based on changes detected in the distributed system. This automated feedback loop maintains data accuracy without requiring manual intervention or inefficient centralized coordination.
3Stability of the object's composition
If centralized repository data is accessed by all migration processes, then data consistency is maintained, but access speed and migration process efficiency decreases
Solution Approach 1:
The patent segments data access by allowing migration processes to query their required fragments from local federated data stores rather than accessing a centralized repository. This maintains consistency through the federated synchronization model while dramatically improving access speed by eliminating network latency to a central location.
Solution Approach 2:
The federated data store architecture acts as an intermediary layer between migration processes and the underlying distributed data fragments. This intermediary maintains consistency through coordinated updates while providing fast local access to migration processes, resolving the contradiction between consistency and speed.
Data Source
AI summary
Repository data fragments distributed across one or both of a first cloud and a network may be accessed. The repository data fragments may be combined into repository data. First resources of the first cloud, a dependency between the first resources, and second resources of a second cloud may be discovered. A migration map between the first cloud and the second cloud may be generated based on the discovered first and second resources and based on the repository data. The first resources may be migrated to the second cloud based on the migration map.