STRUCTURAL OPTIMIZATION OF NEURAL NETWORK FOR QUALITATIVE EVALUATION METHOD OF IT-INFRASTRUCTURE FUNCTIONING
Background. For today, the unit neural networks are widely used to solve various problems. In this regard, the issue of developing learning algorithm that would be able to optimize the structure of neural networks dynamically is very important. The existence of such a method would allow the researcher to get the structure of the neural network that would be available input data quickly. IT infrastructure allows an organization to deliver IT solutions and services to its employees or customers and is usually internal to an organization and deployed within owned facilities
Objective. In most cases, organizations are able to manage individual elements within their IT infrastructure. But, previously, it is necessary to estimate current quality level of service (QoS) or IT infrastructure functioning generally. In a complex IT infrastructure with mutual influence of its elements it is hard to estimate a quality or its operating.
Method. Taken into a large number of IT infrastructure elements it is important to choose a structure of neural networks automatically. The proposed method makes it possible to control processes inside IT-infrastructure and to form the control actions taking into account the quality of the functioning of IT infrastructure components which makes it advisable to use a control loop
targeted at improving the quality indicators of the performance of the IT-infrastructure.
Results. This paper proposes to use neural networks to evaluate a quality of IT infrastructure functioning. Since the task of determining the structure of the neural network is almost impossible, because it requires a deep analysis of each process taking place in the IT infrastructure, the paper proposes to define the structure of the neural network automatically using structural optimization algorithm of neural network. Series of experiments constructed algorithm that demonstrate the ability to use it in problems of classification of data.
Conclusions. The proposed method makes it possible to control processes inside IT-infrastructure and to form the control actions taking into account the quality of the functioning of IT-infrastructure components which makes it advisable to use a control loop targeted at improving the quality indicators of the performance of the IT-infrastructure. Also the resulting structure of neural
network can be used in a quality estimation of functioning of similar IT infrastructure elements. This will allow the service provider “on the fly” construct and retrain its existing models in a shorter period.
Key words: IT infrastructure; neural networks; the control actions; quality estimation.
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