# Evaluation of a New-Concept Secure File Server Solution
## 2 Background
### Review of File System-Related Security Solutions and Research
Based on the approximate value and the arithmetic properties of RNA, the AR-RRNA method was introduced to detect and correct errors and verify computational results. They also provided a theoretical basis for estimating data loss probability and proper management of redundancy, coding rates and computing at various objective preferences, operational loads, and properties. They concluded that with the proper set of RRNS parameters, their suggested scheme both improved the security and reliability of storages and enabled them to reduce the processing time of encrypted data while requiring fewer resources. [PERSON] et al. [PERSON] et al. (2019) presented the design of a file system mechanism in their 2015 paper that protects the integrity of application data from errors such as process crashes, kernel panics, and power failures. In their solution, a simple interface guarantees for the applications that the application data in a file always reflect the last successful fsync or msync operation performed on the file. In addition, their proposed file system provides a new sync mechanism that captures changes in multiple files in case of failure. Error injection tests have demonstrated that their file system protects the integrity of application data from crashes, and performance measurements have confirmed that the implementation is efficient. Their file system runs on traditional hardware and an unmodified Linux kernel and is commercially available. [PERSON] et al. 's solution can be implemented in any file system that supports write(able) snapshots per file. They have also shown that a mechanism for Consistently Modifying Persistent Data of the Application (CMADD) can be easily implemented by file-by-file cloning, a feature already available in AdvFS and in many other modern FSs. Their implementation of O_ATOMIC provides a simple interface for applications, makes file modifications error-atomic by both write and map, avoids duplicate writes, and supports higher volume and frequent transactional updates of application data. Furthermore, their sync implementation supports error-atomic updates of application data in multiple files. Their empirical results showed that the O_ATOMIC implementation of AdvFS preserves the integrity of application data during updates in the case of both crashes following external interventions and accidental crashes (e.g., power outages). Their performance evaluation showed that the cost of introducing O_ATOMIC would be tolerable, especially for large updates. The implementation of the CMADD mechanism into the file system contributes to better acceptance as it requires neither special hardware nor a modified operating system kernel. The authors believed that file systems should implement simple, generic, robust CMADD mechanisms, as this would allow for many applications to take advantage of this feature, which would then be widely available. They further found that O_ATOMIC and syncv are convenient interfaces to use. In Table 1, the advanced data recovery solutions discussed above are compared based on their features. [PERSON] et al. (2012) worked on bandwidth optimization of the Hadoop Distributed File System (HDFS). HDFS is a networked file system used to support several widely used big data processing frameworks, and it can scaled across large clusters. In their 2019 study, they evaluated the effectiveness of using in-network caching on HDFS-supported clusters to reduce in-network bandwidth consumption on switches. They discovered that some applications contained large amounts of data requested by multiple clients and that by caching data reads within the network, the average bandwidth usage of read operations per link in these applications could be more than halved. It was also found that the cache replacement policy chosen can have a significant impact on cache efficiency in this environment, with Low Inter-reference Recency Set (LIRS) and Adaptive Replacement Cache (ARC) generally performing best for the largest and smaller cache sizes.