Data Manager Policy Upgrade for Resource Mobility
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Solution Overview
Problem
In distributed computing systems, resources such as virtual machines and databases may move between resource groups, leading to potential violations of data management service level agreements (SLAs) due to changes in data management policies, resulting in inadequate protection and increased risk of data loss.
Innovation Solution
A processor-based system, such as a data manager, detects resource movements between resource groups and upgrades the data management policy of the destination group to ensure stronger protection, maintaining SLAs by applying a more robust data management policy if necessary, and alerts administrators to these changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resources move between resource groups in a distributed computing system, then resource flexibility and adaptability are improved, but data management policy compliance and SLA adherence deteriorate
Solution Approach 1:
The system continuously monitors resource movements between resource groups and automatically triggers policy evaluation and upgrading processes. When a resource moves to a destination group with weaker data management policies, the system detects this change and initiates feedback loops to upgrade the destination group's policies, ensuring SLA compliance is maintained without restricting resource mobility.
Solution Approach 2:
The data management policies are made dynamic rather than static. The system automatically adjusts and upgrades data management policies in destination resource groups based on the policies of source groups and SLA requirements. This dynamic adaptation allows resources to move freely while the system continuously optimizes policy strength to maintain reliability and compliance.
2Reliability
If data management policies are upgraded to provide stronger protection, then data reliability and security are improved, but system complexity and operational overhead increase
Solution Approach 1:
The system performs self-service by automatically detecting resource movements, evaluating policy gaps, and upgrading data management policies without requiring manual administrator intervention for each individual upgrade. The automated policy evaluation and upgrading mechanisms handle the complexity internally, reducing the operational burden on administrators while maintaining strong data protection.
Solution Approach 2:
The system performs preliminary actions by pre-defining multiple data management policy templates with varying strengths and pre-establishing policy upgrade paths. When a resource moves, the system can quickly apply pre-prepared policy upgrades rather than creating policies from scratch, reducing the complexity and time required for policy management while ensuring adequate data protection.
Data Source
AI summary
Examples described herein relate to a method for managing data management policies of resources. An example includes detecting a movement of a resource from a first resource group protected by a first data management policy to a second resource group protected by a second data management policy. Further, in response to detection of the movement of the resource, a data management policy of the second resource group may be upgraded if first data management policy provides an enhanced level of copy data management over the second data management policy.