| main| | new issue| | archive| | editorial board| | for the authors| | publishing house| |
 Ðóññêèé |
 |
 |
 |
 |
 |
 |
|
|
ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 6. Vol. 25. 2019
A. B. Barsky, D. Sc., Professor, e-mail: arkbarsk@mail.ru, D. I. Melnik, PhD, Senior Researcher, e-mail: mdi_dim@mail.ru, V. V. Pirozhnik, PhD, Senior Researcher, e-mail: vovanina56@mail.ru, Scientific Research Institute (Moscow) Central Research Institute VVKO, Ministry of Defense of Russia
Model of Quality Management of Computing Facilities of a Distributed Multi-Channel Queuing System
The indicators of the quality of computational facilities that control a distributed multi-channel queuing system are given. The main indicator of quality, ensuring the use of computational tools in control systems, is the actual performance. It is determined by control or functional tasks. For the proposed projects of computing means or projects of their modernization as a result of tests or by an expert, the expected quality characteristics are found. According to them is the rating of such projects when they are used in a distributed multi-channel queuing system. Rating is found by associative comparison with known, proposed or conditional projects of a known rating. The rating may take the following values: high, high enough, taking into account possible improvements, medium successful, suggesting further development of scientific and technical research, medium, indicating a wrong or technologically inaccessible way to choose a solution, but deserving attention for lack of other choice, low at which development must be rejected.
The decision system is built on a logical neural network to assess the quality of the processor (microprocessor), a logical neural network to assess the quality of integration of the VS and VC, a logical neural network to assess the quality of the network interaction of computing facilities, and a logical neural network for expert and difficult to verify evaluations of the quality of computing means.
Keywords: peak performance, real performance, structural reliability, functional reliability, safe computing, neural network rating system
P. 358–367