ПОВЫШЕНИЕ ПРОИЗВОДИТЕЛЬНОСТИ РАСПРЕДЕЛЕННЫХ ВЫЧИСЛИТЕЛЬНЫХ СИСТЕМ С ДИНАМИЧЕСКОЙ АРХИТЕКТУРОЙ С ПОМОЩЬЮ НЕЙРОННЫХ МЕТОДОВ ПРОГНОЗИРОВАНИЯ
Анотація
Distributed Computing Systems with Dynamic Architecture
Productivity Increasing Using Neural Networks Methods
The one of progressive ways to solve the problem of constantly growing data processing is to build high-performance distributed environments which include the number of personal devices having tangible computational power in total. Nowadays a lot of so-called volunteer computations implementations are known but still there's a challenge of effective tasks distribution between such client devices because of its non-stable participation. The work is dedicated to increasing of distributed computing systems with dynamic architecture productivity using neural network methods.
Посилання
What is HTCondor? [Электронный ресурс] – Режим доступа: https://research.cs.wisc.edu/htcondor/description.html. / – Электрон. текстовые данные (дата обращения: 08 марта 2016).
OS Platform Statistics – [Электронный ресурс] – Режим доступа: https://www.w3schools.com/browsers/browsers_os.asp – Электрон. текстовые данные (дата обращения: 26.03.2017).
Microsoft Windows – [Электронный ресурс] – Режим доступа: http://research.cs.wisc.edu/htcondor/manual/v8.7/7_2Microsoft_Windows.html – Электрон. текстовые данные (дата обращения: 26.03.2017).
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Авторське право (c) 2017 Роман Михайлович Мазанка, Микола Олександрович Алєксєєв
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