Designing a monitoring model for cluster super–computers

Recently there has been an increase in the number of cyber attacks against computational systems. Growth in the amounts of information that passes through computational clusters and savings on staff requires an application of effective means of monitoring computational resources for the purpose of prediction and elimination of cyber attacks. An analysis of hacker attacks revealed that the break-in was not detected by technical equipment. We examined a concept of building existing systems of monitoring of cluster super-computers. Deficiencies are established in the monitoring systems, which lead not only to the reduction in efficiency of computational clusters but to their safety violations. We described a formal model for the detection of anomalies in the functioning of a computational cluster. The model is the sets of the states of the system depending on functional tasks, it separates processes of targeted functioning of the system from the interface processes of interaction with the network infrastructure and provides for the possibility of their use in neural network technology for detecting anomalies in the functioning of a computational cluster. This model makes it possible to locally control parameters for each process and, based on the formed vector, to detect anomalous influence on the system as a whole. Data of the study can be used for the improvement of already existing subsystems of monitoring of super-computer technologies, as well as form a foundation for creating fundamentally new neural network multi-agent system of monitoring of the detection of anomalous incidents in the performance of computational clusters.