Klaipėdos universitetas

Cloud computing can be defined as a new style of computing in which dynamically scala-ble and often virtualized resources are provided as a services over the Internet. Advantages of the cloud computing technology include cost savings, high availability, and easy scalability. Voas and Zhang adapted six phases of computing paradigms, from dummy termi-nals/mainframes, to PCs, networking computing, to grid and cloud computing. There are four types of cloud computing: public cloud, private cloud, hybrid cloud and community. The most common and well-known deployment model is Public Cloud. A Private Cloud is suited for sensitive data, where the customer is dependent on a certain degree of security.

The paper proposes a technology for mass optimization of two-dimensional body applying genetic algorithms. Main attention is focused on geometry of 2D body, i. e. search for optimal coordinates of body points. Direct analysis of 2D body – von Mises stress determination – is performed using original program based on finite element method. The set of design parameters contains the coordinates of body points in 2D space. The results of numerical experiments proved the proposed technology to be efficient tool for solution of 2D body mass optimization problem.

In this paper a stochastic adaptive method has been developed to solve stochastic linear problems by a finite sequence of Monte-Carlo sampling estimators. The method is based on the adaptive regulation of the size of Monte-Carlo samples and a statistical termination procedure taking into consideration statistical modelling accuracy. Our approach distinguishes itself by the treatment of accuracy of the solution in a statistical manner, testing the hypothesis of optimality according to statistical criteria, and estimating confidence intervals of the objective and constraint functions. To avoid “jamming” or “zigzagging” solving a constraint problem we implement the ε–feasible direction approach. The proposed adjustment of a sample size, when it is taken inversely proportional to the square of the norm of the Monte-Carlo estimate of the gradient, guarantees convergence a. s. at a linear rate. The numerical study and examples in practice corroborate theoretical conclusions and show that the developed procedures make it possible to solve stochastic problems with sufficient accuracy by the means of an acceptable size of computations.