SDNAS Replication Recovery Through Platform Failure Isolation
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Solution Overview
Problem
In SDNAS platforms, the disconnect between SDNAS and underlying storage array platforms in representing replication states and errors leads to difficulties in resuming failed replication operations, necessitating customer escalation and prolonged data unavailability.
Innovation Solution
Implementing SDNAS middleware that monitors replication workflows, distinguishes between SDNAS and underlying platform failures, performs health checks, and executes recovery operations based on platform-specific rules to recover replication failures autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SDNAS monitors replication workflows and implements autonomous recovery mechanisms, then system reliability and productivity are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by monitoring replication workflows continuously and pre-establishing recovery rules for different failure scenarios. When a failure is detected, the pre-configured recovery mechanisms are immediately triggered, enabling autonomous recovery without customer escalation and improving system reliability.
Solution Approach 2:
The patent introduces an intermediary layer (SDNAS middleware) between the customer and the underlying storage platform. This intermediary monitors replication workflows, detects failures, and executes recovery operations autonomously based on pre-configured rules, thereby improving reliability while shielding customers from system complexity.
2Measurement precision
If the system performs detailed health validation and platform-specific failure distinction, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments failure detection into distinct categories: SDNAS platform failures versus underlying storage platform failures. By dividing the monitoring scope into separate domains with specific health checks for each, the system achieves precise failure identification while managing complexity through modular organization of detection routines.
Solution Approach 2:
The patent applies local quality by implementing platform-specific health checks tailored to each layer's characteristics. The SDNAS layer has its own set of health validation rules, while the underlying storage platform has separate checks, allowing precise failure detection at each level without requiring a single complex universal checking mechanism.
Data Source
AI summary
An example methodology includes monitoring a replication workflow execution on a storage system and, responsive to a detection of a failure of the replication workflow, determining whether the failure is in a Software Defined Network Attached Storage (SDNAS) or in an underlying storage array platform. The method also includes, responsive to a determination that the failure is in the SDNAS, validating health of the SDNAS for performing the replication workflow and, responsive to validating the health of the SDNAS, performing recovery of the failed replication workflow. The method further includes, responsive to a determination that the failure is in the underlying storage array platform, determining that the underlying storage array platform can be rolled back to a previously known good state for performing the replication workflow, performing a rollback of the underlying storage array platform to the previously known good state, and performing the recovery of the failed replication workflow.