Abstract
This article deals with the creation of new methods and algorithms for resource scheduling in heterogeneous distributed computing systems using the example of cloud computing environments (CCEs) that reduce the execution time of many incoming tasks by using those computing resources which give the highest real performance in relation to a specific task received. To this end, it is proposed to apply a multiagent approach to organizing the scheduling process: each element of the CCE includes a software agent that has the most complete and up-to-date information about the features of its computer, and the set of these agents jointly select the most appropriate tasks and subtasks taking into account the available information. The principles of construction and the method of operation of the adaptive multiagent CCE resource manager and the algorithms for the operation of resource agents and tasks are described, and the efficiency of the algorithms developed is studied using a distributed software model.
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REFERENCES
Alam, T., Cloud computing and its role in the information technology, IAIC Trans. Sustainable Digital Innovation (ITSDI), 2020, vol. 1, no. 2, pp. 108–115.
Bataev, A.V., Assessment of the global cloud technology market in the financial sector, Vektor Ekon., 2019, no. 6, pp. 91–91.
Karaev, A.V., Emel’yanov, D.O., and Baranovskaya, T.P., Relevance and features of the implementation of IT services using cloud technologies, in Informatsionnoe obshchestvo: sovremennoe sostoyanie i perspektivy razvitiya (Information Society: Current State and Development Prospects), 2020, pp. 387–390.
Fink, A. and Homberger, J., An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives, Flexible Serv. Manuf. J., 2013, vol. 25, no. 1, pp. 94–121.
Verma, A. and Kaushal, S., A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling, Parallel Comput., 2017, vol. 62, pp. 1–19.
Yuan, X., Liu, J., and Wimmers, M.O., A multi-agent genetic algorithm with variable neighborhood search for resource investment project scheduling tasks, IEEE Congr. Evol. Comput. (CEC), IEEE, 2015, pp. 23–30.
Bertsekas, D.P., Feature-based aggregation and deep reinforcement learning: a survey and some new implementations, IEEE/CAA J. Autom. Sin., 2019, vol. 6, no. 1, pp. 1–31.
Habibi, F., Barzinpour, F., and Sadjadi, S., Resource-constrained project scheduling task: review of past and recent developments, J. Proj. Manage., 2018, vol. 3, no. 2, pp. 55–88.
Mao, H., Alizadeh, M., Menache, I., and Kandula, S., Resource management with deep reinforcement learning, HotNets 2016—Proc. 15th ACM Workshop on Hot Topics in Networks, 2016. pp. 50–56.
Xue, L., Sun, C., Wunsch, D., et al., An adaptive strategy via reinforcement learning for the prisoner’s dilemma game, IEEE/CAA J. Autom. Sin., 2018, vol. 5, no. 1, pp. 301–310.
Zhan, Y., Ammar, H.B., and Taylor, M.E., Theoretically-grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer, IJCAI Int. Joint Conf. Artif. Intell., 2016, vol. 2016-Janua, pp. 2315–2321.
Wang, H., Huang, T., Liao, X., et al., Reinforcement learning for constrained energy trading games with incomplete information, IEEE Trans. Cybern., 2017, vol. 47, no. 10, pp. 3404–3416.
Zheng, L., Yang, J., Cai, H., et al., Magent: a many-agent reinforcement learning platform for artificial collective intelligence, Proc. AAAI Conf. Artif. Intell., 2018, vol. 32, no. 1, pp. 8222–8223.
Lowe, R., Wu, Y.I., Tamar, A., et al., Multi-agent actor-critic for mixed cooperative-competitive environments, Adv. Neural Inf. Process. Syst., 2017, vol. 30, pp. 1–12.
Kalyaev, I.A., Kalyaev, A.I., and Korovin, Ya.S., Algorithm for multi-agent resource scheduling in a heterogeneous cloud environment, Vychisl. Tekhnol., 2016, vol. 21, no. 5, pp. 38–53.
Kalyaev, A.I. and Kalyaev, I.A., Method of multiagent scheduling of resources in cloud computing environments, J. Comput. Syst. Sci. Int., 2016, vol. 55, no. 2, pp. 211–221.
Kalyaev, I.A. and Kapustyan, S.G., A method of multiagent management of “smart” Internet production, Robototekh. Tekh. Kibern., 2018, no. 1, pp. 34–48.
Ablyalimov, O.S., On solving an optimization problem by dynamic programming method, Universum: Tekh. Nauki, 2020, no. 9-1(78), pp. 16–18.
Kantsedal, S.A. and Kostikova, M.V., Dynamic programming for the traveling salesman problem, Avtom. Sist. Upr. Prib. Avtom., 2014, no. 166, pp. 15–20.
Kolemaev, V.A., Matematicheskaya ekonomika (Mathematical Economics), Moscow: UNITY, 2002.
Kalyaev, А.I. and Korovin, Y.S., Adaptive multiagent organization of the distributed computations, AASRi Procedia, 2014, vol. 6, pp. 49–58. http://dx.doi.org/10.1016/j.aasri.2014.05.008
Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C., Introduction to Algorithms. 2nd ed. http://www.mif.vu.lt/valdas/ALGORITMAI/LITERATURA/Cormen/Cormen.pdf .
Reingold, E., Nievergelt, J., and Deo, N., Combinatorial Algorithms: Theory and Practice, Englewood Cliffs: Prentice Hall, 1977. Translated under the title: Kombinatornye algoritmy. Teoriya i praktika, Moscow: Mir, 1980.
Kalyaev, A.I. and Khisamutdinov, M.V., The program and methods of experimental research of methods and algorithms for the operation of a distributed computing system using a software model, in Nauka i sovremennost’: sb. mater. V Mezhdunar. nauchno-prakt. konf. (Science and Today’s World: Collect. Mater. V Int. Sci. Pract. Conf.) 2016, pp. 44–45.
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The work was carried out within the framework of the State Order for Peter the Great St. Petersburg Polytechnic University, project no. 075-01429-22-02.
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Translated by V. Potapchouck
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Kalyaev, I.A., Kalyaev, A.I. Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments. Autom Remote Control 83, 1228–1245 (2022). https://doi.org/10.1134/S0005117922080069
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DOI: https://doi.org/10.1134/S0005117922080069