Storage Volume Clustering by Workload Fingerprints
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Solution Overview
Problem
Managing resources allocated to storage volumes in a storage system is challenging due to the blending effect of workloads from requesters with different profiles, leading to insufficient or excessive resource allocation, which affects performance and efficiency.
Innovation Solution
A storage system workload management process that assigns workload fingerprints to each storage volume, groups them into clusters based on similarity, and manages resources within these clusters by throttling workloads, adjusting resource allocation, and migrating data to optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If resources are allocated to storage volumes without workload analysis, then resource allocation is simplified, but resource allocation efficiency deteriorates due to blending effect of different workload profiles
Solution Approach 1:
The patent segments storage volumes into different workload categories by analyzing workload fingerprints and grouping volumes with similar workload profiles together. This segmentation allows the system to apply different resource allocation strategies to different workload types, improving overall resource allocation efficiency while maintaining manageable complexity through automated classification.
2Measurement precision
If workload fingerprints are assigned and clustering is performed, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting workload metrics, generating workload fingerprints, and clustering storage volumes without requiring manual intervention. The workload management system autonomously analyzes workload patterns and applies appropriate resource allocation policies, reducing the operational complexity burden on administrators while maintaining high measurement precision.
3Productivity
If resource allocation is increased for all storage volumes, then performance is improved, but resource utilization efficiency deteriorates due to excessive allocation
Solution Approach 1:
The patent applies local quality by allocating resources according to the specific workload characteristics of each storage volume cluster. Instead of uniform resource allocation, the system tailors resource allocation to match the actual performance needs of different workload types, ensuring that each cluster receives appropriate resources without over-provisioning, thereby improving both performance and resource utilization efficiency.
Data Source
AI summary
In some examples, a system assigns workload fingerprints to each respective storage volume of a plurality of storage volumes, the workload fingerprints assigned to the respective storage volume across a plurality of points. Based on the workload fingerprints assigned to respective storage volumes of the plurality of storage volumes, the system groups the storage volumes into clusters of storage volumes. The system manages an individual cluster of the clusters of storage volumes according to an attribute associated with the individual cluster.