Web server overload identification by use of neural network

Problems of diagnosing web servers’ overloading are considered. It was found out that the use of simple models doesn't allow achievement of exact overload identification, but the use of complex models helps to assess the acceptable performance level of identification algorithm. When using a real Web server a set of features inherent to specific hardware-software platform can have a direct impact on its ability to resist overloads. The approach based on the server overload identification with the use of a neural network with the architecture of the three-layer direct distribution preceptor is offered. The range of the data source, necessary for construction of the set of input data of a neural network, is designated. The algorithm of actions for preparation of basic data, their analysis, and application of the received results for prediction of the origin of overloads on a Web server is offered. The offered approach is tested by data received during load testing of the Web server. The obtained results demonstrated that the offered approach is facilitative in a more precise overload assessment, which helps to organize effective requests’ management with the purpose of congestion avoidance.