Data Storage Node Position Adjustment for Synchronization Overtime Prevention
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Solution Overview
Problem
In data storage, users face challenges in setting an accurate Recovery Point Objective (RPO) value, leading to risks of 'overtime' synchronization, where automatic data synchronization exceeds the specified RPO time, potentially causing data unavailability or loss due to high system performance and large difference data replication.
Innovation Solution
A method is introduced to adjust the storage position of target objects from source storage nodes to candidate storage nodes with better performance, identified through historical synchronization data analysis, to prevent overtime synchronization events by transferring servers to idle or high-performance nodes before actual synchronization starts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data synchronization is performed with high system performance and large difference data replication, then data replication speed is improved, but synchronization time exceeds the specified RPO time
Solution Approach 1:
The system performs preliminary prediction of synchronization time before actual data replication. By analyzing historical synchronization data and current system state, the method predicts whether the synchronization will complete within the RPO time. This preliminary action allows the system to identify potential overtime risks before they occur, enabling proactive intervention such as adjusting replication strategies or alerting operators to prevent RPO violations.
2Reliability
If automatic data synchronization is performed within specified RPO time, then data availability is improved, but system complexity increases due to prediction and dynamic adjustment mechanisms
Solution Approach 1:
The system implements a feedback mechanism where historical synchronization data is continuously collected and analyzed. The prediction module uses this feedback to assess current synchronization risks, and the system dynamically adjusts replication parameters based on predicted outcomes. This closed-loop feedback ensures data availability by preventing RPO violations while managing complexity through automated decision-making based on historical patterns.
3Productivity
If storage position is adjusted from source storage node to target storage node, then synchronization performance is improved, but data migration time is consumed
Solution Approach 1:
The system performs storage position adjustment as a preliminary action before critical synchronization operations. By proactively migrating data to optimal storage nodes in advance, the system prepares the infrastructure to handle upcoming replication tasks more efficiently. This preliminary repositioning reduces the risk of synchronization overtime by ensuring data is located on high-performance storage nodes before replication is needed.
Data Source
AI summary
Data storage is presented herein. For instance, an example method includes determining, in response to that a predicted backup time of a target object is longer than a threshold time, a target server and a source storage node which are corresponding to the target object, wherein the target server stores objects, and the target server is associated with the source storage node. storage nodes whose predicted backup time for an object is shorter than a corresponding threshold time are determined as candidate storage nodes. A target storage node is determined based on different pieces of performance of the candidate storage nodes. A storage position of the target object is adjusted from the source storage node to the target storage node. Consequently, position distribution of servers on a storage node cluster can be adjusted so as to avoid a synchronization overtime event as much as possible.