Study the efficiency of using multi-agent models in modern microservice architectures
Abstract
The purpose of this work is to explore the possibility and ways of applicability of multi-agent models in modern microservice architectures, since today programmers and architects increasingly prefer this approach. To achieve the goal of the study, the article considers the main features of the development of multi-agent systems, and assesses the applicability and expected positive effect when building microservices using agents. As a result, an architecture was developed for the implementation of a ready-made complex for diagnosing thyroid diseases. The results of applying the proposed tools and architecture confirmed the correctness of using a multi-agent approach to build microservices and demonstrated the effectiveness of such a solution.
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Tarasov, V. B. (1998). Agents, multi-agent systems, virtual communities: a strategic direction in computer science and artificial intelligence. Artificial Intelligence News, (2), 5-63.
Li Y. et al. Learning distilled collaboration graph for multi-agent perception //Advances in Neural Information Processing Systems. – 2021. – vol. 34. – pp. 29541-29552.
Plappert S., Gembarski P. C., Lachmayer R. Multi-agent systems in mechanical engineering: a review //Agents and Multi-Agent Systems: Technologies and Applications 2021: Proceedings of 15th KES International Conference, KES-AMSTA 2021, June 2021. – Singapore : Springer Singapore, 2021. – pp. 193-203.
Russell, S. (2006). Artificial intelligence. Modern approach.
Ageev S.A., Saenko I.B. Management of information security risks of a secure multi-service special-purpose network based on intelligent multi-agents. // Bulletin of the Yugra State University, 2020 Issue 3 (58). pp. 47–52
Graça G. Microservices Architecture. [Electronic resource]: https://habr.com/ru/company/vk/blog/320962/ (Date of access: 02/10/2023).
Radchenko G.I. Service-Oriented Architecture [Electronic resource]: https://glebradchenko.susu.ru/courses/bachelor/odp/2013/SUSU_Distr_08_SOA_02.pdf (Date of access: 01/25/2023).
Burlutsky, V. V., Keramov, N. D., Baluev, V. A., Izert, M. I., & Yakimchuk, A. V. (2020). DEVELOPMENT OF A MULTI-AGENT INTELLIGENT SYSTEM FOR SOLVING THE PROBLEMS OF CLASSIFICATION AND RANKING OF MATERIALS ON THE INTERNET. Bulletin of the Yugra State University, (3 (58)), 47-52.
Merenkov, D. N. (2021). ENSURING INFORMATION SECURITY OF A SYSTEM MODEL WITH MICROSERVICE ARCHITECTURE. Innovation. The science. Education, (26), 1658-1671.
Samokhin, N. Yu., Oreshkin, A. A., & Suprun, A. S. (2019). Implementation of a data exchange protocol between software agents in a cloud infrastructure in geographically distributed data processing centers. Scientific and technical bulletin of information technologies, mechanics and optics, 19(6), 1086-1093.
Bogdanova, V. G., & Pashinin, A. A. (2018). Development of a self-organizing multi-agent system for decentralized control of a distributed solution of applied problems. Information and Mathematical Technologies in Science and Management, (3 (11)), 115-126.
Oparin, G. A., Bogdanova, V. G., & Pashinin, A. A. (2019). Control of pipeline-parallel computing in solving problems of qualitative research of binary dynamical systems based on the Boolean constraint method. Information and mathematical technologies in science and management, (3 (15)), 79-90.
Gorodetsky, V. I., Grushinsky, M. S., & Khabalov, A. V. (2015). Multi-agent systems: a review of the current state of theory and practice. with the support of the Russian Foundation for Basic Research (grant, (96-01).
Botz B. European Thyroid Association TIRADS, 2021 [Electronic resource]: https://radiopaedia.org/articles/european-thyroid-association-tirads (Date of access: 12/17/2022).
Bertoncelli C. M. et al. PredictMed-epilepsy: A multi-agent based system for epilepsy detection and prediction in neuropediatrics //Computer Methods and Programs in Biomedicine. – 2023. – Vol. 236. – С. 107548.
Lee B. et al. Generative design for COVID-19 and future pathogens using stochastic multi-agent simulation //Sustainable Cities and Society. – 2023. – С. 104661.
Su H. et al. EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system //Transportation Research Part C: Emerging Technologies. – 2023. – Vol. 146. – С. 103-955.
Chu T. et al. Multi-agent deep reinforcement learning for large-scale traffic signal control //IEEE Transactions on Intelligent Transportation Systems. – 2019. – Vol. 21. – Issue. 3. – pp. 1086-1095.
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