Storage reliability of massive amounts of data is the basis for deep learning, and continuous data protection (CDP) technology is an effective means of ensuring it. The method of storing CDP historical data, represented by TRAP-Array parity logs, effectively reduces the overheads of storage capacity, but it increases the risk of historical data loss due to the absence of the RAID protection mechanism for the parity data. This paper proposes a design method for a fault-tolerant and energy-efficient continuous data protection system (FTEECDP), which is composed of SSD mirror array, TRAP parity generation logic and S-RAID disk array. Taking the SSD mirror array as the source data volume of the system to provide the I/O service of the upper applications, FTEECDP improves the throughput of the system. This method also takes S-RAID as the CDP log volume to store the TRAP parity logs. When the disk data error occurs, the error data will be recovered from the data redundancy information of S-RAID, thus protecting the TRAP parity chain from being destroyed and improving the reliability of the TRAP parity logs. The experiment shows that in the continuous data protection system, the CDP logs stored as the TRAP parity can greatly reduce the storage space overheads and reduce the system energy consumption. In addition, with the disk scheduling algorithm, S-RAID shifts the disk grouping without any data requests into a standby state, thus further reducing the energy consumption of the system.
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May 31, 2020
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