Mapping Table for Distributed Storage Migration
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Solution Overview
Problem
Current distributed storage systems face challenges in seamlessly migrating applications and data between different computing platforms, leading to downtime and inefficiencies in managing large-scale data deployments across various cloud and on-premise environments.
Innovation Solution
The implementation of an orchestration layer that manages bundled applications with roles, provisioning, configuration parameters, and action hooks, along with a mapping table that tracks storage segments and their locations across multiple computing platforms, enables efficient migration and management of applications and data across distributed storage systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If applications and data are migrated between different computing platforms in traditional distributed storage systems, then platform flexibility and adaptability are improved, but migration downtime and system unavailability increase
Solution Approach 1:
The system performs preliminary actions by creating and maintaining mapping tables that pre-establish the relationship between storage segments and their locations across multiple computing platforms. When migration is needed, the system can quickly update these pre-existing mappings rather than discovering locations during migration, thereby reducing downtime. The orchestration layer prepares the environment in advance by maintaining awareness of data locations across platforms.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that manages the complexity of cross-platform data access. This intermediary maintains mapping tables that abstract the physical location of storage segments from applications, allowing seamless migration between platforms. The mapping table acts as a mediator between the application and the physical storage location, enabling platform flexibility without requiring applications to be aware of migration events.
2Productivity
If data is distributed across multiple computing platforms, then system scalability and resource allocation are improved, but system complexity and management difficulty increase
Solution Approach 1:
The system segments data into discrete storage segments and maintains separate mapping table entries for each segment. This segmentation allows the system to manage large distributed datasets by breaking them into manageable units, each with its own location tracking. The orchestration layer can independently manage individual segments across platforms without requiring full system reconfiguration, thereby improving scalability while keeping management complexity manageable through modular organization.
Solution Approach 2:
The mapping table structure serves multiple functions: it tracks storage segment locations, enables cross-platform data access, supports migration operations, and provides a unified interface for applications regardless of underlying platform diversity. This multi-functionality reduces the need for separate management systems for each platform, thereby improving resource allocation efficiency while managing overall system complexity through a universal management approach.
3Adaptability or versatility
If traditional storage migration methods are used, then platform independence is improved, but data accessibility and application continuity during migration deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the orchestration layer continuously maintains and updates mapping tables that reflect the current state of storage segments across platforms. This real-time feedback on data locations enables the system to redirect access seamlessly during migration operations, ensuring data accessibility is maintained. The mapping table provides continuous feedback about where data resides, allowing applications to access data regardless of platform changes.
Solution Approach 2:
The mapping table acts as an intermediary that decouples applications from physical storage locations and computing platforms. During migration, the intermediary can be updated to reflect new locations while applications continue to access data through the same logical interface. This intermediary layer ensures platform independence while maintaining data accessibility by absorbing the complexity of migration operations without exposing them to applications.
Data Source
AI summary
An application executing on a first computing platform includes containers executing role instances. One or more logical storage volumes include segments allocated to the application and references in a mapping table. The mapping table indicates a tier ID corresponding to the computing platform on which each segment is located. A snapshot of the application may be restored on a second computing platform. The mapping table may be copied to the second computing platform and used without transferring segments from the first computing platform to the second computing platform. Reads will be routed to the first computing platform using the tier ID in the mapping table.