Dynamic Backup Block Size Adjustment for Storage Efficiency
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Solution Overview
Problem
Existing data storage systems face inefficiencies in incremental backup processes due to suboptimal block size and backup interval settings, leading to inefficient data transfer and increased overhead in primary storage systems.
Innovation Solution
The primary storage system analyzes host write patterns and transfer characteristics to dynamically adjust block size and backup intervals, categorizing writes as sequential, random, or default to optimize data block formation and transfer efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed block size and backup interval settings are used in incremental backup, then the backup process is simple to implement, but data transfer efficiency is reduced due to suboptimal settings
Solution Approach 1:
The patent implements dynamic adjustment of block size and backup interval based on host write patterns. The system categorizes writes as sequential, random, or default, and automatically adjusts parameters accordingly. This transforms the static backup configuration into a dynamic system that adapts to changing workload characteristics, thereby improving data transfer efficiency without requiring manual intervention.
Solution Approach 2:
The system monitors host write patterns and transfer characteristics, using this feedback to continuously optimize block size and backup interval settings. By analyzing the characteristics of host writes and adjusting parameters based on observed performance, the system creates a closed-loop control mechanism that improves efficiency while maintaining operational simplicity.
2Productivity
If larger block sizes are used for data transfer, then fewer blocks need to be transferred, but more unnecessary data is included in each transfer
Solution Approach 1:
The patent applies different block sizes to different types of host writes based on their characteristics. Sequential writes receive larger block sizes to reduce the number of transfer operations, while random writes use smaller block sizes to minimize unnecessary data transfer. This localized optimization ensures that each transfer operation is tailored to the specific characteristics of the data being transferred.
Solution Approach 2:
The system dynamically changes the block size parameter based on the type of host write detected. By adjusting this critical parameter according to write patterns (sequential, random, or default), the system optimizes the balance between reducing transfer operation count and minimizing unnecessary data transfer volume.
3Loss of substance
If smaller block sizes are used for data transfer, then less unnecessary data is transferred per block, but more transfer operations are required
Solution Approach 1:
The system applies smaller block sizes specifically to random writes where precision is critical, while using larger block sizes for sequential writes where transfer efficiency is paramount. This localized approach ensures that unnecessary data transfer is minimized for random writes without excessively increasing the total number of transfer operations.
Solution Approach 2:
The block size parameter is dynamically adjusted based on write pattern analysis. For random writes, smaller block sizes are selected to reduce unnecessary data transfer, while for sequential writes, larger block sizes are used to reduce the number of operations. This parameter adaptation resolves the contradiction by making block size context-dependent.
4Reliability
If frequent backups are performed, then data protection is improved, but transfer overhead and system resource consumption increase
Solution Approach 1:
The system dynamically adjusts the backup interval based on host write patterns and transfer characteristics. Rather than performing frequent backups at fixed intervals, the system adapts the backup frequency to match actual data change patterns, thereby maintaining data protection while reducing unnecessary transfer overhead and system resource consumption.
Solution Approach 2:
The system uses feedback from monitoring transfer characteristics and host write patterns to optimize backup timing. By analyzing when data changes occur and adjusting backup intervals accordingly, the system maintains adequate data protection while minimizing transfer overhead and resource usage.
Data Source
AI summary
Techniques for transferring modified data with efficiency are described herein. According to one embodiment, the method starts with analyzing host write data to determine a host write pattern of a host writing to sectors at a primary storage, and then a data block size and a backup interval are determined based on at least one of the host write pattern, wherein a data block includes a number of sectors that is determined based on the host write pattern, and known characteristics of transferring dirty data blocks of the primary storage, wherein a dirty data block includes at least one sector that has been changed from a previous backup. Then dirty data blocks are transferred from the primary storage to a secondary storage as part of a backup.