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Machine learning in a multi-agent system for distributed computing management

Авторы: Bychkov I.V., Feoktistov A.G., Sidorov I.A., Edelev A.V., Gorsky S.A., Kostromin R.O.

Журнал: CEUR Workshop Proceedings, Proc. 4th Intern. Conf. on Information Technology and Nanotechnology

Том: 2212


Год: 2018

Отчётный год: 2018


Местоположение издательства:


Аннотация: We address the relevant problem of machine learning in a multi-agent system for distributed computing management. We propose a new approach to the agent learning in the system for managing job flows of scalable applications in a heterogeneous distributed computing environment, which includes high-performance computing clusters, as its main components. We manage parameter sweep applications that execute their jobs in a virtual machine environment. We use the specialized tools to implement such environment. In contrast to the known approaches, our approach is based on the integrated applying of methods for job classification and parameter adjustment of algorithms for functioning agents. Simulation modeling the environment allows eliciting the necessary knowledge for parameter adjustment. During the learning of agents, we use the expert knowledge of environment node administrators. An example of solving the complex practical problem that relates to studying energy development directions of Russia demonstrates advantages of the proposed approach.

Индексируется WOS: 0

Индексируется Scopus: 1

Индексируется РИНЦ: 1

Публикация в печати: 0

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