Dynamic Data Volume Migration in Multi-Tenant Cloud Storage
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Solution Overview
Problem
In cloud computing environments, users face challenges in managing operational parameters of shared data storage resources, such as storage capacity and performance, which can lead to increased complexity and latency when scaling or modifying allocated resources, often requiring complex application logic and data migration across multiple volumes or remote data stores.
Innovation Solution
The system allows users to dynamically modify operational parameters of provisioned data volumes with minimal disruption, automatically migrating data and adjusting resource commitments, enabling users to resize storage capacity, performance levels, and other parameters without reconfiguring applications, by utilizing algorithms to allocate suitable resources and manage data volumes across multiple systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If users self-administer migration of data to a larger volume, then storage capacity is increased, but device complexity and latency increase due to managing data across multiple volumes
Solution Approach 1:
The system enables users to self-administer volume resizing operations through automated processes. When a user requests to increase storage capacity, the system automatically provisions additional volume space and migrates data without requiring user intervention in the complex processes of data segmentation, distribution, and tracking across multiple volumes.
Solution Approach 2:
The system introduces an intermediary management layer that handles the complexity of data migration and volume management. This intermediary automatically manages the processes of partitioning data, distributing segments across volumes, and tracking data locations, shielding users from these complex operations while enabling capacity expansion.
2Volume of stationary object
If users add new volumes to supply additional storage capacity, then storage capacity is increased, but productivity decreases due to increased latency in managing data across multiple volumes
Solution Approach 1:
The system automatically performs data migration and management tasks when volumes are added or resized. The automated processes handle data segmentation, distribution, and tracking without requiring user intervention, thereby maintaining high productivity despite increased storage capacity and complexity.
Solution Approach 2:
The system performs preliminary actions by pre-configuring volume structures, pre-segmenting data, and pre-establishing tracking mechanisms before users need to access the expanded storage. This preparation work is done automatically in the background, minimizing latency when users access data across multiple volumes.
3Volume of stationary object
If users partition data file into several smaller segments and distribute across several volumes, then storage capacity is increased, but device complexity increases due to additional data overhead required to keep track of distributed segments
Solution Approach 1:
The system automatically manages the segmentation and distribution of data across volumes. It self-administers the creation of segment tracking structures and maintains metadata about data locations without requiring user configuration. This automated approach increases storage capacity while minimizing the complexity burden on users.
Solution Approach 2:
The system introduces an intermediary management layer that handles the complexity of data segmentation and tracking. This intermediary automatically creates and maintains the data structures needed to track distributed segments, shielding users from the complexity of managing data overhead while enabling efficient use of expanded storage capacity.
Data Source
AI summary
Customers of shared resources in a multi-tenant environment can modify operational parameters of electronic resources. A customer can be provisioned a data volume of a specified size, storage type (e.g., hard disk drive or solid state device), committed rate of input/output operations per second, and/or geographical location, for example. The customer can subsequently modify any such operational parameters by submitting an appropriate request, or the operational parameters can be adjusted automatically based on any of a number of criteria. Data volumes for the customer can be migrated, split, or combined in order to provide the shared resources in accordance with the modified operational parameters.