THEORETICAL FOUNDATIONS OF CREATING SELF-ORGANIZING DISPATCHERS OF DISTRIBUTED SYSTEMS BASED ON A MULTI-AGENT SOCIO-INSPIRATIONAL APPROACH

  • A.I. Kalyaev South Federal University
Keywords: Distributed systems, socioinspired approach, bioinspired approach, multi-agent system, decentralized system

Abstract

This article describes new principles of organization, methods and algorithms for the functioning
of the Distributed System (DS) dispatcher, which allow allocating and reallocating resources
with dynamically changing parameters between incoming tasks in order to minimize their
execution time. The main problem that does not allow today to effectively estimate the execution
time of tasks in a heterogeneous DS directly follows from the distribution of the system: each of its
elements has partial independence and may differ significantly from others, moreover, in the process
of operation, its capabilities may change, and all this is essential. affects the efficiency of
distribution of tasks between DS nodes and the time it takes to complete tasks. The article proposes
a new approach to organizing a DS dispatcher, based on the application of the theory of multiagent
systems and socio-inspirational (based on accepted in human society) methods: DS users
place their tasks on special nodes – bulletin boards, a proactive software agent is placed on each
DS node, which implements constant monitoring of the parameters of your site and search on message
boards suitable for solving problems. At the same time, the agents participating in the solution
of the common task form communities in which they plan the process of solving the task and
the distribution of parts of the tasks to minimize the delay time for their solution. As a criterion for
the effectiveness of the DS, it was decided to take the value of the average delay in the execution of
functional tasks relative to the required points in time, respectively, the agents distribute tasks in
such a way as to minimize the value of the specified criterion. This article includes an introduction,
a formal statement of the task of scheduling DS resources, a review of existing approaches to
organizing a DS dispatcher, a description of the proposed multi-agent solution to the task of
scheduling DS resources using a socio-inspirational approach, an algorithm for the operation of a
distributed system and its elements, a description of the application of a socio-inspirational approach
in relation to the task scheduling process. and conclusion. The main advantages of the
proposed approach include: the ability to use reliable and up-to-date information about the specialization
and current performance of resources in dispatching; high fault tolerance due to the
absence of DS elements, failure of which leads to a complete loss of DS performance; the possibility
of flexible scaling of the DS (increasing the number of resources), achieved by decentralizing
the dispatching process.

References

1. Shamakina A.V. Obzor tekhnologiy raspredelennykh vychisleniy [Overview of distributed
computing technologies], Vestnik YuUrGU. Seriya: Vychislitel'naya matematika i informatika
[Bulletin of SUSU. Series: Computational Mathematics and Computer Science], 2014, No. 3.
Available at: https://cyberleninka.ru/article/n/obzor-tehnologiy-raspredelennyh-vychisleniy
(accessed 18 August 2021).
2. Smelyanskiy R.L. Model' funktsionirovaniya raspredelennykh vychislitel'nykh sistem [Model
of functioning of distributed computing systems], Vestnik Moskovskogo universiteta [Bulletin
of the Moscow University], 1990, Vol. 15, pp. 3-21.
3. Karpov V.E. Kollektivnoe povedenie robotov. Zhelaemoe i deystvitel'noe [Collective behavior
of robots. Wishful and valid], Sovremennaya mekhatronika: Sb. nauchn. trudov Vserossiyskoy
nauchnoy shkoly (g. Orekhovo-Zuevo, 22-23 sentyabrya 2011) [Modern mechatronics: Collection
of scientific papers of the All-Russian Scientific School (Orekhovo-Zuyevo, September
22-23, 2011)]. Orekhovo-Zuevo, 2011, pp. C132.
4. Daneev A.V., Basyrov A.G., Mastin A.B. Dispetcher energosberegayushchego parallel'nogo
vychislitel'nogo protsessa [Dispatcher of an energy-saving parallel computing process],
Sovremennye tekhnologii. Sistemnyy analiz. Modelirovanie [Modern technologies. System
analysis. Modeling], 2010, No. 3. Available at: https://cyberleninka.ru/article/n/dispetcherenergosberegayuschego-
parallelnogo-vychislitelnogo-protsessa (accessed 18 August 2021).
5. Kalyaev A.I., Kalyaev I.A. Method of multiagent scheduling of resources in cloud computing
environments, Journal of Computer and Systems Sciences International, 2016, Vol. 55, No. 2,
pp. 211-221. DOI: 10.1134/S1064230716010081.
6. Ivashchenko A.V. i dr. Mul'tiagentnye tekhnologii dlya razrabotki setetsentricheskikh sis-tem
upravleniya [Multi-agent technologies for the development of network-centric control systems],
Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2011, No. 3 (116).
7. Gorodetskiy V.I., Bukhvalov O.L., Skobelev P.O. Sovremennoe sostoyanie i perspektivy
industrial'nykh primeneniy mnogoagentnykh sistem [The current state and prospects of industrial
applications of multi-agent systems], Upravlenie bol'shimi sistemami: Sb. trudov [Managing
large systems: Proceedings], 2017, No. 66.
8. Dechesne F., Ghorbani A., Yorke-Smith N. Introduction to the special issue on agent-based
modelling for policy engineering, AI Soc. Springer, 2015, Vol. 30, No. 3, pp. 311-313.
9. Kulinich A.A. Model' komandnogo povedeniya agentov v kachestvennoy semioticheskoy srede. Ch.
1. Kachestvennaya sreda funktsionirovaniya. Osnovnye opredeleniya i postanovka zadachi [The
model of team behavior of agents in a qualitative semiotic environment. Part 1. Qualitative functioning
environment. Basic definitions and problem statement], Iskusstvennyy intellekt i prinyatie
resheniy [Artificial intelligence and decision-making], 2017, No. 3, pp. 95-105.
10. Karpov V.E. Modeli sotsial'nogo povedeniya v gruppovoy robototekhnike [Models of social
behavior in group robotics], Upravlenie bol'shimi sistemami: sbornik trudov [Management of
large systems: proceedings], 2016, No. 59.
11. Kalyaev A. et al. An effective algorithm for multiagent dispatching of resources in heterogeneous
cloud environments, 2016 5th International Conference on Informatics, Electronics and
Vision (ICIEV). IEEE, 2016, pp. 1140-1142.
12. Kalyaev I.A., Kalyaev A.I., Korovin I.S. Multiagent Resource Dispatching in a Heterogeneous
Cloud Environment, 2019 International Conference on Electronical, Mechanical and Materials
Engineering. Atlantis Press, 2019, pp. 79-85.
13. Kalyaev I.A., Kalyaev A.I., Korovin I.S. A Modified Method of Multiagent Resource Dispatching
in a Heterogeneous Cloud Environment, 2019 International Conference on Electronical,
Mechanical and Materials Engineering (ICE2ME 2019). Atlantis Press, 2019, pp. 72-78. DOI:
10.2991/ice2me-19.2019.17.
14. Gerasimov B.N., Morozov V.V., yakovleva N.G. Sistemy upravleniya: ponyatie, struktura,
issledovanie [Management systems: concept, structure, research]. Samara SGAU, 2002.
15. Gorodetskiy V.I. Samoorganizatsiya i mnogoagentnye sistemy. I. Modeli mnogoagentnoy
samoorganizatsii [Self-organization and multi-agent systems. I. Models of multi-agent selforganization],
Izvestiya Rossiyskoy akademii nauk. Teoriya i sistemy upravleniya [Proceedings
of the Russian Academy of Sciences. Theory and control systems], 2012, No. 2, pp. 92.
16. Legovich Yu.S., Maksimov D.Yu. Vybor ispolnitelya v gruppe intellektual'nykh agentov
[Choosing a performer in a group of intelligent agents], Upravlenie bol'shimi sistemami: Sb.
trudov [Managing large systems: a collection of works], 2015, No. 56.
17. Istomin V.V. Prognozirovanie povedeniya grupp avtonomnykh intellektual'nykh agentov na
osnove teorii mnogoagentnykh sistem [Predicting the behavior of groups of autonomous intelligent
agents based on the theory of multi-agent systems], Inzhenernyy vestnik Dona [Engineering
Bulletin of the Don], 2011, Vol. 18, No. 4.
18. Campbell A., Wu A.S. Multi-agent role allocation: issues, approaches, and multiple perspectives,
Auton. Agent. Multi. Agent. Syst. Springer, 2011, Vol. 22, No. 2, pp. 317-355.
19. Gaaze-Rapoport M.G., Pospelov D.A. Ot ameby do robota: modeli povedeniya [From amoeba
to robot: behavioral models]. Ripol Klassik, 1987, 150 p.
20. Conte R. et al. Sociology and social theory in agent based social simulation: A symposium //
Comput. Math. Organ, Theory. Springer, 2001, Vol. 7, No. 3, pp. 183-205.
Published
2021-11-14
Section
SECTION I. DISTRIBUTED COMPUTING SYSTEMS