Neural Network Approach to Forecasting of IT Service Quality

Each IT service tends to solve its specific task with some predefined level of quality. In addition, providers of such services assure end users with some quality level according to the bucket user has bought. However, in order to provide those services at stated level it is important to know which kind of IT infrastructure does the provider use and which mathematical models can be applied to the hardware functioning, which is used for creating IT infrastructure. This article suggests using of artificial neural networks for classification and forecasting problems, which appear in IT infrastructure during provisioning of IT services. This can be made with indirect connection between IT resources usage and quality of service. Each IT service can have its own quality, which can be evaluated based on subjective and objective indicators of their performance. General problem, which can be solved in scope of the topic, is regression prediction problem, which can be perfectly solved with the use of neural networks. This paper presents neural network approach with decomposed groups of quality indicators, which implies breaking down IT infrastructure into hierarchical levels and defining quality indicators on each level with the further use of multilayer perceptron and recurrent neural network. Experimental results were compared with each other and have proven their effectiveness. The advantage of the use of neural networks in proposed problem is in small decline of predicted results from actual data.