Backup Server Concurrent Decompression for Data Synchronization
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Solution Overview
Problem
Current disaster recovery solutions for data backup from a production server to a backup server consume significant network bandwidth and processor resources due to the need for data compression and decompression, which can slow down the update process.
Innovation Solution
A system and method that dynamically invoke multiple compression and decompression programs concurrently on both the production and backup servers based on available processor resources and network bandwidth thresholds to expedite the data update process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data compression is performed before downloading to reduce data volume, then network bandwidth consumption is reduced, but processor resource consumption increases
Solution Approach 1:
The system dynamically adjusts compression operations based on real-time monitoring of processor resource availability and network bandwidth conditions. When processor resources are abundant and network bandwidth is constrained, compression is performed. When processor resources are scarce, compression is skipped or reduced, allowing the system to adapt to changing operational conditions and balance the trade-off between network bandwidth consumption and processor resource consumption.
Solution Approach 2:
The system changes operational parameters (compression level, compression algorithm selection, or whether to compress at all) based on the current state of processor resources and network bandwidth. This allows flexible adjustment of the compression process to optimize the balance between reducing network bandwidth consumption and minimizing processor resource consumption under different system conditions.
2Productivity
If multiple compressed data files are decompressed concurrently to speed up the update process, then productivity increases, but processor resource consumption increases
Solution Approach 1:
The system dynamically determines the number of decompression operations to execute concurrently by monitoring available processor resources. When processor resources are abundant, more decompression operations are performed in parallel to maximize data update speed. When processor resources are limited, the system reduces the degree of parallelism or queues decompression operations, thereby balancing productivity gains with processor resource consumption.
3Loss of energy
If data is compressed and transferred to backup server to reduce network bandwidth usage, then network bandwidth consumption is reduced, but the decompression process at backup server consumes significant processor resources
Solution Approach 1:
The backup server dynamically adjusts its decompression operations based on available processor resources. When processor resources are abundant, the backup server performs decompression operations concurrently to minimize the time data remains in compressed format and to speed up the synchronization process. When processor resources are constrained, the backup server reduces the degree of parallel decompression or prioritizes critical data files, thereby balancing network bandwidth efficiency with processor resource consumption at the backup server.
Data Source
AI summary
System, method and program product for backing up a plurality of data files from a first server to a second server via a network. A determination is made that more than one compressed data file at the second server, downloaded by the first server, is waiting to be decompressed. A determination is made whether an amount of available processor resource in the second server exceeds a predetermined threshold. If the amount of available processor resource in the second server exceeds the predetermined threshold, a plurality of data decompression programs are invoked in the second server to decompress the plurality of compressed data files substantially concurrently, and data updates in the decompressed data files are applied to corresponding files in the second server.