Storage Node Grouping for Load Balancing and Data Protection
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Solution Overview
Problem
Existing data storage systems face instability and overload when a node fails, leading to poor predictability of data loss and resource utilization, causing instability and increased complaints from users.
Innovation Solution
A data storage method where all devices constitute a single data pool, with a management server that polls and selects a group of devices for data storage, redistributing data to prevent overload and ensuring decentralized storage across multiple nodes, thereby sharing access traffic and maintaining stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all access traffic is taken over by remaining nodes in a cluster when a node fails, then data protection is achieved, but node stability deteriorates due to overload
Solution Approach 1:
The patent segments the cluster into multiple sub-clusters, where each sub-cluster independently manages its own data and has dedicated backup nodes. When a node fails, only the affected sub-cluster experiences load redistribution, while other sub-clusters remain unaffected. This segmentation prevents system-wide overload and maintains node stability while still achieving data protection within each sub-cluster.
2Reliability
If data is stored in clusters with redundant nodes, then data protection is improved, but resource utilization deteriorates due to low efficiency
Solution Approach 1:
The patent implements local quality by allowing different sub-clusters to have different data storage configurations and backup strategies tailored to their specific requirements. Each sub-cluster can optimize its resource allocation independently, improving overall resource utilization while maintaining data protection. The system assigns backup nodes locally within sub-clusters rather than requiring global redundancy, reducing wasted resources.
3Reliability
If multiple nodes store identical data in a cluster, then data protection is achieved, but predictability of data loss deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor the status of nodes and data storage across sub-clusters. This feedback enables the system to accurately track which data resides on which nodes and to predict the impact of potential failures. The management server uses this information to provide deterministic calculations of data loss scenarios, improving predictability while maintaining data protection through redundant storage in sub-clusters.
Data Source
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AI summary
A data storage method, a management server, a storage equipment and system are provided in the present invention, in which the data storage method includes that all n equipments used for storing data constitute a data pool; when needing to store data, all equipments of the data pool are inquired one by one to select m equipments as one group, and then the said data are stored in the m equipments of the selected group respectively, herein m is larger than 1 and smaller than n. The embodiments of the present invention can resolve the problems that the load of the other nodes is increased and the other nodes are insecure because some node is failed, the availability of every node is low and the predictability of every node is worse in the current data storage manner; the embodiments of the present invention can average the load of the equipment and insure the higher stability of the node when some node is failed, and improve the source availability and predictability of the node.