# Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
## 3 Related Work
The proposed model calculates the weight of the evaluation value of set of indicators such as the evaluation values of memory size, disk space, CPU and read-write speed of the disk, then the best set of replica nodes will be selected using the comprehensive evaluation value. The authors in [37] proposed a hybrid data replication system for edge servers and cloud infrastructure which reduce the latency perceived by read and update operations by locating the replica near the end user. This paper proposed a replica convergence algorithm which keeps replicas in both the cloud and the edge server by combining Conflict-free Replicated Data Types (CRDTs) and Operational Transformation (OT) to achieve a consistency model. The proposed model is suitable for the applications based on microservices as it follows the hierarchical architecture of a master replica which broadcasts updates received from a particular replica. The authors in [38] proposed a partial storage strategy for cloud data centers by partitioning the dual direction download of files from different cloud storage. The proposed model introduced an improvement to the dual directional algorithm (DDFTP) which is used as a file retrieval from the cloud server. This algorithm divides the data into blocks. Then each block will be assigned to two cloud servers based on the download history of these blocks which in turn will be used to download the data from opposite directions. The download process will be parallel by handling the assignment of the forward and backward in the block, and the proposed portioning technique removes some blocks from each replica based on the history download experience. The proposed model did not take into consideration many factors such as memory speed, size, server failure and fault tolerance issues which affect the effectiveness and efficiency of the system. In [39] the authors proposed an improved cache utilisation model which identifies user hot-spots, where fit clients are selected for partial caching. The proposed model uses a location tracking and prediction method to identify hot-spot locations to be used later by service's subscribers. The client nodes in the proposed model works as service providers and coupled with subscribers' latency. The authors in [40] proposed a collaborative fog-to-fog communication algorithm which allow fogs to communicate with each other to process incoming tasks. In this proposed model, a threshold is set for maximum delay allowed. If the delay of the assigned fog reaches the threshold, then the fog will check the list of available candidate fogs which can service it and delegate the task to this candidate. In [41] the authors proposed a fog resource selection algorithm (FResS) which enables automated fog selection and allocation, the performance data of each fog are stored in standard format as execution logs. For an incoming job to be executed, these logs will be used for predicting its run-time to have real time estimate for best fog selection.In [42] the authors proposed a neural network prediction model to predict replica locations using the files' access profile. The run-time prediction model is meant to generate file location predictions for incoming tasks using historical executions. It utilises data clustering techniques to separate related tasks from the history and generates a prediction of a file's location using a mean predictor. On the other hand, the proposed model takes some factors into consideration to minimise a cost function using storage cost, host access cost and communication cost to achieve the minimum cost of the data service. In addition, the proposed model calculates the cost difference between replicating a whole file and a segment of a file on a certain node.