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An NVM-based Efficient Write-Reduction Scheme for Block Device Driver Performance Improvement
http://doi.org/10.5626/JOK.2019.46.10.981
Recently, non-volatile memory (NVRAM) has attracted substantial attention as a next-generation storage device due to the fact that it shows higher read/write performance than flash-based storage as well as higher cost-effectiveness than DRAM. One way to use NVRAM as a storage device is to modify the existing file system layer or block device layer. Leveraging the NVRAM block device driver is advantageous in terms of overall system compatibility, as it does not require any modification of the existing storage stack. However, when considering the byte-level addressing of the NVRAM device, the block write is not effective in terms of durability or performance. In this paper, we propose a block device driver that attempts to optimize the existing block write operations while considering the existing functionalities of the file system. The proposed block write reduction scheme provides a partial block write by classifying the type of blocks according to the structure of the file system as well as the amount of data modified in the block using XOR operation. Several experiments are performed to validate the performance of the proposed block device driver under various workloads, and the results show that, compared to the conventional block write operations, the amount of writes is reduced by up to 90%.
An Effective Technique for Detecting Vulnerabilities in Android Device Drivers
Android- and Linux-based embedded systems require device drivers, which are structured and built in kernel functions. However, device driver software (firmware) provided by various 3rd parties is not usually checked in terms of their security requirements but is simply included in the final products, that is, Android-based smart phones. In addition, static analysis, which is generally used to detect vulnerabilities, may result in extra cost to detect critical security issues such as privilege escalation due to its large proportion of false positive results. In this paper, we propose and evaluate an effective technique to detect vulnerabilities in Android device drivers using both static and dynamic analyses.
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