# Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
## 3 Related Work  
The authors in [22] proposed a replica creation and selection model using the replica creation based on access tendency (DRC-AT), and the replica selection based on response time (DRS-RT). The proposed model calculates the size of the file access tendency, which is used for the replica creation decision, and the user's request time is analysed and evaluated to select the best node that contains the requested data by the tasks submitted by the user. The authors in [23] proposed a replica replacement algorithm which predicts the future usage of the replica using the weight value and normalisation by calculating the three factors, cost of replica, frequency and number of requests. The proposed model uses a prediction function that computes the relative worth of replica using the history of the file access, then it determines the priority of the replica placement. The authors in [24] proposed an algorithm for accessing a subset of a spatial replica using a greedy approach which chooses replica subsets which allow fast data access to maximise performance. Storing the replica subset in descending order by calculating the goodness value of each subset. The proposed model aiming to provide load balancing by taking into consideration the hardware performance, filesystem and file storage prefetching. The authors in [25] investigate the effect of replication role in Distributed Transactional Memory (DMT) and comparing a full replication scheme and partial replication approaches. The study shows that the partial replication approaches have an obvious improvement in terms of scalability by reducing the amount of transferred and stored data at each node. To ensure data consistency, the study mentioned several existing techniques such as single copy model [26; 27], Distributed Multiversioning (DMV) system model [28], History-based multiversioning [29] and Clock Validation [30; 31]. This study also investigates the latency issues in partial replication in the context of DMT. The authors in [32] proposed a nonlinear integer programming model for data replication in cloud storage aiming to achieve a low cost and high availability. The proposed low-cost failure resilient replication scheme is handling both non-correlated and correlated machine failure. The proposed model shows an improvement in data availability and replica consistency cost compared to Random Replication (RR) [33], Copyset Replication [33] and Replication Degree Customisation (RDC) [34]. The proposed scheme assigns a portion replica for each data object and the popularity of the replica is considered by handling the aforementioned problems. The popularity of any two data objects are analysed and compared to reduce the replication cost. The authors in [35] investigate the use of object storage system in the cloud storage environment to store and retrieve unstructured data in cloud computing environment. The object storage system process data in a different way compared to modern storage system which process data as objects instead of blocks and files. All data types can be stored in an object, such as records, file, database, medical record, video, audio and images, or it can be used to store only an entire type of data. This paper analysed the different types of storage systems used in cloud computing environment, the authors claiming that the object storage is the most suitable storage system for unstructured static data to be used in cloud storage environment due to its scalability, flexibility and security features. The authors in [36] proposed a replica placement model based on evaluating the comprehensive performance value of the node. The proposed model used the Hadoop Distributed File System (HDFS) as the base model to be improved which divide the data into small blocks as the basic storage unit to be stored on different distributed nodes.