Problem statement and methods for the computing resources distribution in geo-distributed heterogeneous computing environments with dynamics and restrictions on the execution time of tasks

An analysis of the presented formulations of problems of distribution of computing resources has shown that to date there is no formalization of taking into account the properties of geodistribution, heterogeneity and dynamics of computing environments, subject to the existence of a limit on the execution time of user tasks. The purpose of this article is to develop a new general formulation of the problem of distribution of computing resources for geo-distributed heterogeneous computing environments with dynamics and a set of methods for solving it. The novelty of the research results is the new formulation of the problem for the specified class of computing environments, which differs from the existing ones by the complex integration of controlled parameters for the use of computing resources for data transit and the computational complexity of the procedure for allocating resources in the formal formulation of the problem of distribution of computing resources, as well as a set of methods for solving the problem, which differ from existing ones, taking into account the parameters of the computational complexity of the procedure for distributing the computational load and the characteristics of nodes in transit sections of the network. Within the framework of the study, discrete optimization methods are used, including iterative stochastic numerical optimization methods. The developed set of methods reduces the use of computing resources during the operation of the computing environment and, as a consequence, other resources that depend on the load on the computing nodes. The experimental results confirm the effectiveness of the developed set of methods, making it possible to reduce the use of computing resources for the process of their allocation by up to 2 times, as well as to reduce the execution time of a set of tasks by up to 2 times while maintaining the level of device load due to the selection of algorithmic implementation of data processing.