Distributed Storage Metrics Recording via Migration Tracking
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Solution Overview
Problem
Conventional data storage systems face scalability issues and data inaccuracies due to centralized recording of storage performance metrics, leading to duplicate metrics during object migration, which can result in inaccurate performance analysis and operational insights.
Innovation Solution
Distributing storage performance metrics recording across multiple storage appliances using a migration status table to track object migrations, allowing for the disregard of duplicate metrics and utilizing a snapshot to prevent data inconsistencies, thereby ensuring accurate performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If performance metrics are recorded into a central database, then all storage performance metrics can be collected in one location, but the architecture does not scale well and creates a bottleneck
Solution Approach 1:
The patent divides the centralized metrics recording system into distributed components. Each storage appliance maintains its own local database for recording performance metrics, eliminating the single-point bottleneck. The bulk metrics service then periodically collects and consolidates these distributed metrics into an archive, achieving both local autonomy and centralized analysis capabilities.
2Measurement precision
If metrics are recorded during object migration, then performance data is captured, but duplicate metrics are archived causing data inaccuracies
Solution Approach 1:
The system performs preliminary actions by establishing migration status tracking before metrics recording occurs. The bulk metrics service checks the migration status table before archiving metrics to determine if an object is currently migrating. If migration is detected, the service skips archiving metrics for that object during the migration window, preventing duplicate entries while maintaining continuous performance monitoring capability.
Data Source
AI summary
A technique of processing storage cluster performance metrics involves obtaining access to performance metrics from storage appliances of a storage cluster, the performance metrics identifying performance for storage objects managed by the storage appliances. The technique further involves, after access to the performance metrics is obtained, disregarding a duplicate set of performance metrics for a storage object that migrates from a first storage appliance of the storage cluster to a second storage appliance of the storage cluster. The technique further involves, after the duplicate set of performance metrics is disregarded, archiving the performance metrics to an archive. After archiving the performance metrics to the archive, the technique may provide a performance analysis based on the performance metrics from the archive as well as adjust operation of the storage cluster according to the performance analysis.