Оптимізація параметрів функціонування системи керування ІТ-інфраструктурою датацентру

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). Разработан алгоритм обучения системы управления датацентров с использованием системы допусков на значения признаков для каждого из классов расспознавания. Это позволяет применить нормированные статистики количества попаданий признаков в поля допусков для определения момента переобучения системы и повысить достоверность решений. Исследована эффективность использования аддитивно-мультипликативной и энтропийной сверток частных критериев качества функционирования датацентраРозроблено алгоритм навчання системи керування датацентром з використанням системи допусків на значення ознак для кожного з класів розпізнавання. Це дозволяє застосувати нормовані статистики кількості потраплянь ознак у поля допусків для визначення моменту перенавчання системи та підвищити достовірність рішень. Досліджено ефективність використання адитивно-мультиплікативної та ентропійної згорток частинних критеріїв якості функціонування датацентр

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

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)

Розробка методу навчання ознак та вирішальних правил для прогнозування порушення умов обслуговування в хмарному середовищі

We developed the algorithm of learning of the multilayer feature extractor based on ideas and methods of neural gas and sparse encoding, for the problem of prediction of violation of agreement conditions on the service level in a cloud-based environment. Effectiveness of the proposed extractor and autoencoder was compared by the results of physical simulation. It is shown that the proposed extractor requires approximately 1.6 times as few learning samples as the autoencoder for construction of error-free decision rules for learning and test samples. This allows us previously put into effect prediction mechanisms of controlling appropriate cloud-based services.To build up decision rules, it is proposed to use transformation of the space of primary features using computationally efficient operations of comparison and "excluding OR" for construction in the radial basis of the binary space of secondary features of separate class containers. In this case, for binary feature encoding, it is proposed to use modification of the population algorithm of search for maximum value of the Kullback’s information criterion. Modification implies consideration of compactness of images in the space of secondary features, which allows increasing the gap between distributions of classes and decreasing the negative effect of overfitting.The authors explored dependence of decision accuracy for training and test samples of the system of prediction of violation of SLA conditions on parameters of the feature extractor and those of the classifier. The extractor configuration, acceptable in terms of accuracy and complexity, was selected. In this case, two time windows, which intersect in time by 50 % and read through 50 features, were used at the entrance of the extractor. The first layer of extractor coding contains 30 basis vectors, and the second layer – 20. Thus, the intralayer pooling and non-linearity were formed by concatenation of sparse codes of each window and by continuation of the resulting code twice as much in order to separate positive and negative code components and to transform the resulting code into the vector of sign-positive features.Разработан алгоритм обучения многослойного экстрактора признаков, использующий принципы нейронного газа и разреженного кодирования. Предложен информационно-экстремальный метод двоичного кодирования признакового представление для построения решающих правил. Это позволяет уменьшить требования к объемам обучающих данных и вычислительных ресурсов и обеспечить высокую достоверность прогнозирования нарушения условий договора об уровне обслуживания в облачной средеРозроблено алгоритм навчання багатошарового екстрактора ознак, що використовує принципи нейронного газу та розрідженого кодування. Запропоновано інформаційно-екстремальний метод двійкового кодування ознакового подання для побудови вирішальних правил. Це дозволяє зменшити вимоги до обсягів навчальних даних і обчислювальних ресурсів та забезпечити високу достовірність прогнозування порушення умов договору про рівень обслуговування в хмарному середовищ