Rule-Based Storage Rebalance in Distributed Clusters
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Solution Overview
Problem
In distributed storage systems, unbalanced storage node clusters lead to inefficient storage and I/O processing due to factors like over-provisioning, volume migration, or node additions/removals, necessitating a mechanism to rebalance storage resources effectively.
Innovation Solution
A rule-based method for rebalancing storage in distributed systems, involving the creation of trigger thresholds, evaluation of metrics, and a rebalance operation that selects candidate pools and moves volumes between them based on provisioning rules and thresholds to maintain balanced storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If storage nodes are added to a distributed storage system, then storage capacity increases, but storage balance deteriorates
Solution Approach 1:
The system continuously monitors storage metrics (used space, available space) across all storage nodes and uses this feedback to automatically trigger rebalancing operations when thresholds are crossed, maintaining storage balance despite node additions
Solution Approach 2:
The storage system performs self-rebalancing by automatically selecting source and target pools, evaluating migration criteria, and executing data movement without external intervention, allowing the system to maintain balance after node additions
2Stability of the object's composition
If storage rebalancing is performed frequently, then storage balance improves, but system complexity increases
Solution Approach 1:
The system pre-establishes trigger thresholds and rebalancing policies before imbalances occur, so when storage metrics cross thresholds, automated rebalancing is already configured and can execute immediately without complex real-time decision-making
Solution Approach 2:
The system uses configurable threshold parameters (trigger thresholds for used space and available space) that can be adjusted based on workload requirements, allowing flexible control of rebalancing behavior without increasing operational complexity
3Stability of the object's composition
If manual rebalancing is performed, then storage balance can be controlled, but operational overhead increases
Solution Approach 1:
The storage system automatically monitors its own state, evaluates imbalance conditions, and executes rebalancing operations without requiring user intervention, significantly reducing operational overhead while maintaining control through configurable thresholds
Solution Approach 2:
The system continuously monitors storage metrics and uses this feedback to automatically trigger rebalancing when necessary, eliminating the need for manual monitoring and intervention while maintaining optimal storage balance
Data Source
AI summary
Distributed storage systems are implemented with rule based rebalancing mechanisms. Methods includes steps for creating a set of rules for rebalancing data storage space in a storage node cluster, as well as steps for performing a rebalance operation across the storage node cluster using the set of rules. The distributed storage systems include one or more labels for storage pools and storage volumes.