The paper reviews a dynamic storage distribution based on cloud technology in data processing centers. The method is proposed for the dynamic distribution of the users' data among storage devices with various characteristics depending on the intensity of the data use. The method provides more efficient use of the storage resources by the users.

The paper reviews dynamic distribution of storage resources among the users in data processing centers. The process of changing memory usage state was revealed to be the process of Markov. The paper proposes the development of stochastic model of the memory and computing usage distribution and the development of probability density functions over practical data. Parameters of probability density functions were defined with the help of stochastic model and practical data. The calculation of the developed model and the parameters of the probability density function is realized dynamically during the ongoing process. At the beginning of each time interval, it is forecasted that the process will be shifted to which state with which maximum probability. The adequacy of the previous forecasts is monitored. Note that, over the time, the quality of the forecast and the level of adequacy increases. The model is used in the virtualization of storage resources usage process and ensures the use of storage resources without wasting. Structure of visualization base is given. The base enables to monitor all stages of the process. Using monitoring base the issues can be resolved to analyze different aspects of the process. Recommendations are given on the use of obtained results.

The paper defines thecondition of allocatingcomputing resources forData Center users or joining new users. In this case, the problems solved at Data Center considered equally complex, and a model of time change is developed, which is spent for the problem solution depending on the possible quantity of parallel processors to solve one problem. The relationship between the time spent on problem solution and the number of parallel processors is defined as a hyperbolic function. Consequently, the condition of allocating resource for a new user at Data Center is defined.

The article reviews the security issues of cloud-based SCADA (Supervisory Control and Data Acquisition) systems that are widely used in monitoring and management of the oil and gas industry. Recently, extensive research has been conducted regarding the migration of SCADA systems to the cloud infrastructure. Transfer of applications used in the management and control of the SCADA system to cloud servers allows for efficient management of the system, solving resource problems and reducing costs. The article analyzes the advantages of cloud-based SCADA systems. Existing gaps have been identified that could obstruct the security of cloud-based SCADA systems. Security problems and risks in the use of cloud-based SCADA systems were analyzed and suggestions were made to help solve them to some extent.

The paper deals with the migration of SCADA (Supervisory Control and Data Acquisition) systems widely used in the monitoring and management of the oil and gas industry to the cloud computing environment. There arise various problems in data collection, transmission, and processing because of traditional SCADA systems being very expensive, inflexible, and complicated scalability. The transferring of the SCADA system's applications to the cloud environment reduces costs and improves scalability. The purchase of hardware and software is carried out at a lower cost than its installation and maintenance. In the article, the usage of cloud-based SCADA systems has been proposed for easy, safe, reliable and quick collection and processing of data from facilities installed in the oil and gas industry.