Cloud Abstraction Layer for Hybrid Data Fabric Management
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Solution Overview
Problem
Current storage systems lack a self-service experience for seamless data placement across local and remote endpoints, requiring IT admin intervention and not supporting bidirectional data movement between on-premises and cloud environments effectively.
Innovation Solution
A system and method that extends a local data fabric to multiple cloud providers using a cloud abstraction layer, data migration module, and management module, enabling seamless data placement and management across endpoints by configuring cloud providers as remote endpoints, migrating data through various mechanisms, and providing unified data management services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional storage systems are used, then data can be stored on-premises with single access method, but bidirectional data movement to cloud and self-service capability are not supported
Solution Approach 1:
The patent introduces a cloud abstraction layer as an intermediary between on-premises storage systems and cloud providers. This abstraction layer enables bidirectional data movement by mediating between different storage environments, allowing data to move seamlessly between cloud and on-premises locations without requiring complex direct integrations. The abstraction layer translates and manages data placement policies, making the system adaptable to multiple cloud providers while maintaining a unified interface.
Solution Approach 2:
The storage system is designed with multi-functionality to support both on-premises and cloud environments through a single unified architecture. The system can act as both a local storage system and a cloud-integrated system, providing bidirectional data movement, self-service capabilities, and unified data management across hybrid environments. This universal design eliminates the need for separate systems for different storage locations.
2Extent of automation
If IT admin intervention is required for data placement, then data management can be centralized, but self-service experience and responsiveness are lost
Solution Approach 1:
The patent implements self-service data placement by enabling the storage system to automatically make intelligent decisions about data location and movement based on predefined policies and current system state. The system can autonomously determine whether data should be placed on-premises or in the cloud, and can automatically execute data movement operations without requiring IT administrator intervention. This maintains user control while achieving high automation.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor data access patterns, storage utilization, and performance metrics. Based on this feedback, the storage system dynamically adjusts data placement decisions and can trigger automated data movement operations. The feedback loop enables the system to respond to changing conditions in real-time, maintaining optimal performance while providing self-service capabilities.
3Quantity of substance
If cloud is used as a data dumping ground, then storage capacity is increased, but seamless data management and consistent QoS are not maintained
Solution Approach 1:
The patent segments the storage system into distinct functional components: on-premises storage resources, cloud storage resources, and a unified data fabric that manages both. This segmentation allows the system to treat different storage locations as separate but equally important resources. The data fabric applies consistent management policies across both segments, ensuring that data can be seamlessly moved between locations while maintaining consistent QoS guarantees and management controls.
Solution Approach 2:
The system merges on-premises and cloud storage resources into a unified hybrid storage pool through the data fabric. This combination allows the system to leverage the unlimited capacity of cloud storage while maintaining the performance and control characteristics of on-premises storage. The unified management layer ensures that data placement, movement, and access policies are consistently applied across both storage types, preventing the cloud from becoming merely a data dumping ground.
Data Source
AI summary
Disclosed is a method and system to provide seamless data placement, data movement, and data management into the cloud. The system includes a processor; and a memory. The memory stores machine-readable instructions that when executed by the processor cause the processor to extend the local data fabric and the services to the cloud providers by utilizing a cloud abstraction layer module. The cloud providers act as remote endpoints configured with a source unit. The process is further configured to implement a data migration in which a data migration module migrates data from the source unit to the cloud providers, and performs a plurality of management activities through an interaction between a control plane and a management module. Then the processor is configured to create a cloud tenant in the source unit to map the cloud providers. The data is migrated to the cloud provider through a plurality of mechanisms selected from at least one of instantiating an application instance into a cloud tenant attached to the cloud provider, and utilizing a plurality of remote storage pools which includes a plurality of remote endpoints as members.