## 3 Related Studies
This is accomplished by the random data distribution function (CRUSH), which is designed for unreliable object storage devices (OSDs). This function replaces the file allocation table. With CEPH, distributed data replication, error detection, and recovery operations are transferred to object storage devices are running on the local file system. Thus, system performance is enhanced. A distributed set of metadata makes its management extremely efficient. The Reliable autonomic distributed object store (RADOS) layer manages all filing processes. Measurements were performed under various workloads to test the performance of CEPH, which can also work within different discs size. As a result, I/O performance is extremely high. It has been shown to have a scalable metadata management. Because of the measurements, it supports 250,000 meta transactions per seconds. With CEPH, a high performance, reliable, and scalable distributed file system has been developed [29]. In 2007, Hadoop was developed, consisting of Hadoop distributed file system (HDFS) and MapReduce-parallel computing tool. Hadoop is a framework that provides analysis and transformation of very large datasets. HDFS distributes big data by dividing into clusters on standard servers. To ensure data security backs the blocks up on the servers by copying them [30]. Hadoop/MapReduce is used to process and manage big data. The'map' function distributes the data on the cluster and provides it to be processed. The'reduce' function ensures the data will be combined. Hadoop has scalability, and it can be easily handle PBs of data [31]. Today, Hadoop is used by many major companies. It is preferred in industrial and academic fields. Companies like LinkedIn, eBay, AOL, Alibaba, Yahoo, Facebook, Adobe, and IBM, use Hadoop generally [32]. Announced in 2015, CalvinFS is a file system that can be scalable and has replica property using a highly efficient database designed for metadata management. For this, it divides the metadata horizontally across multiple nodes. The file operations that need to edit the metadata item works distributed. This system also supports standard file systems. This file system approach has shown that scaling can be done up to billions of files. While reducing reading delays, it can conduct hundreds of thousands of updates and millions of reads per second at the same time [33]. In 2016, [PERSON] and [PERSON] presented a scalable distributed system framework [34]. The system performs scaling on the central server. The proposed framework transfers the data processing work to other computers, by the server as the amount of data collected increases. In other words, the system works distributed when several of data increases. Other frameworks like this work are: IdeaGraph algorithm [35], probabilistic latent semantic analysis (PLSA) [36], locality-aware scheduling algorithm [37], nearest neighbor algorithm [38], item-based collaborative filtering algorithm [39], recommendation algorithm [40], convex optimization [41], and parallel two-pass MDL (PTP-MDL) [40]. [PERSON] et al. designed an efficient storage table for electronic health records. The system also makes analysis and produce detailed statistical values by using MapReduce. It is designed with HBase, a distributed-column based database that runs on the Hadoop distributed file system MapReduce framework. The model is low cost and has two name nodes. In addition, HMaster and HRegionServer allow load balancing to get better performance. However, it has been noted that the system should work to develop data block replication strategies for HDFS [41]. Today, distributed file systems can be considered/examined in two main categories:  
* Big data storage: Use necessary file system and cluster schema to save big data (data save). * Big data analytics: The short and consistent analysis of the data collected from the nodes by grid computing tools (data mining).