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In the cloud environment, ensuring a high level of data security is in high demand. Data planning storage optimization is part of the whole security process in the cloud environment. It enables data security by avoiding the risk of data loss and data overlapping. The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient. In our work, we propose a data scheduling model for the cloud environment. The model is made up of three parts that together help dispatch user data flow to the appropriate cloud VMs. The first component is the Collector Agent which must periodically collect information on the state of the network links. The second one is the monitoring agent which must then analyze, classify, and make a decision on the state of the link and finally transmit this information to the scheduler. The third one is the scheduler who must consider previous information to transfer user data, including fair distribution and reliable paths. It should be noted that each part of the proposed model requires the development of its algorithms. In this article, we are interested in the development of data transfer algorithms, including fairness distribution with the consideration of a stable link state. These algorithms are based on the grouping of transmitted files and the iterative method. The proposed algorithms show the performances to obtain an approximate solution to the studied problem which is an NP-hard (Non-Polynomial solution) problem. The experimental results show that the best algorithm is the half-grouped minimum excluding (HME), with a percentage of 91.3%, an average deviation of 0.042, and an execution time of 0.001 s.

Intelligent Solution System for Cloud Security Based on Equity Distribution: Model and Algorithms