Optimizing the Parameters of Functioning of the System of Management of Data Center IT Infrastructure

Abstract

The information-extreme algorithm was developed of machine learning of the management system of a data center for predicting violations of the SLA terms. The scheme of binary encoding of features is considered, where the code of features is determined by the results of control of belonging of its value to the appropriate field of tolerances of each class of recognition. According to the data of tracing the work of virtual machines of a data center, we formed learning samples and synthesized decisive rules, optimal in information sense. The increase in reliability of decisive rules by 8 % is demonstrated, as compared to results of learning by the well-known scheme, where the control tolerances on the attributes' values are defined only for one single base class.We proposed to use extreme serial statistics in the form of normalized statistics of the numbers of the attributes' values entering their fields of control tolerances for determining the moments of retraining a management system that allows adapting to the change in patterns of consumption of resources of a data center.The efficiency of additive-multiplicative and entropy convolutions of the partial criteria of quality of functioning of a data center was examined to form the fitness function of swarm algorithm of optimization of the plan to deploy virtual machines of a data center. It is proved by the results of physical modeling that the additive–multiplicative convolution is more efficient on the stage of growth in the load of a data center, while the entropic convolution has highee efficiency during reduction in the load of a data center. In both cases, the decrease in operating expenses of a data center is observed in comparison to the known MBFD algorithm (Modified Best Fit Decreasing)