BIOINSPIRED METHOD FOR CLASSIFICATION OF DISTRIBUTED RESOURCES FOR DISPATCHING IN GRID-COMPUTING
Abstract
The article is devoted to solving the problem of scheduling distributed computing resources based on their classification by the bioinspired search method to improve the efficiency of gridcomputing functioning. The relevance of the problem is justified by a significant increase in the demand for the paradigm of distributed computing in conditions of information overflow and uncertainty. The article deals with the problems of scheduling heterogeneous computing resources when solving complex professional and scientific problems arriving at different points in time, based on the classification according to significant signs of resource compliance and readiness. A comparative review of existing analogues is carried out. The formulation of the problem to be solved in the context of the selected research topic is formulated. The strategy of choosing bioinspired modeling for solving the problem has been substantiated. The aspects of various decentralized bioinspired methods effectiveness of the use are analyzed. It is proposed to solve the problem of scheduling computational resources based on determining the correspondence of the resource to the required class. The classification is carried out on the basis of the bioinspired optimization method application, built on the basis of the Fish School Search algorithm. The use of the population bioinspired method allows us to provide unprecedented parallelism in obtaining alternative solutions and to optimize the distribution of available computing resources depending on the sets of significant features. The object of the research is the processes of data classification, which include ordered sequences of actions aimed at the distribution of computing resources by classes of problems to be solved. The subject of the research is bioinspired methods for solving the problem of data classification in grid-computing. To evaluate the effectiveness of the proposed method, a software application was developed and a computational experiment was carried outwith a different number of computing resources generated classes. Each computing resource has a certain set of attributes, which is a vector of its features. The cosine measure of the similarity between a resource attributes vector and a certain class attributes vector is a classification criterion. To improve the quality of the dispatching process, the task of classifying computing resources is solved for a variety of options for organizing the flows of complex tasks to be solved in gridcomputing. The obtained quantitative estimates demonstrate the time savings in solving the problems of scheduling distributed computing resources based on their classification by the bioinspired search method at least 7 %. The time complexity in the considered examples was . The described studies have a high level of theoretical and practical significance and are directly related to the solution of artificial intelligence classical problems.
References
grid computing, Numerical analysis and scientific computing. – CRC Press, UK, 2009.
2. Magoulès F. (ed.). Fundamentals of grid computing: theory, algorithms and technologies, Numerical
analysis and scientific computing. – CRC Press, UK, 2010
3. Patel S. Survey Report of Job Scheduler on Grids, International Journal of Emerging Research
in Management &Technology, 2013, No. 2 (4), pp. 115-125.
4. Li M., Baker M. The grid: core technologies. – John Wiley & Sons Ltd, England, 2005.
5. Saak A.E., Kureichik V.V., Kravchenko Y.A. Scheduling quality of precise form sets which
consist of tasks of circular type in GRID systems, Journal of Physics: Conference Series,
2018, 1015 (4).
6. Saak A.E., Kureichik V.V., Lezhebokov A.A. Scheduling of parabolic-type tasks arrays in GRID
systems, Advances in Intelligent Systems and Computing, 2017, pp. 292-298.
7. Saak A., Kureichik V., Kravchenko Y. To scheduling quality of sets of precise form which consist
of tasks of circular and hyperbolic type in grid systems, Advances in Intelligent Systems
and Computing, 2016, pp. 157-166.
8. Saak A.E., Kureichik V.V., Kuliev E.V. Ring algorithms for scheduling in grid systems, Advances
in Intelligent Systems and Computing, 2015, pp. 201-209.
9. Kravchenko Y.A., Kravchenko D.Y., Kursitys I.O. Architecture and method of integrating information
and knowledge on the basis of the ontological structure, Advances in Intelligent Systems
and Computing. 1st International Conference of Artificial Intelligence, Medical Engineering,
and Education, AIMEE 2017. Moscow: 2018, Vol. 658, pp. 93-103.
10. Kravchenko Y.A., Kureichik V.V. Knowledge management based on multi-agent simulation in
informational systems, Conference proceedings. 8th IEEE International Conference “Application
of Information and Communication Technologies – AICT 2014”. 15-17 October 2014,
Astana, Kazakhstan, pp. 264-267.
11. Monteiro R.P., Verçosa L.F.V., Bastos-Filho C.J. A. Improving the Performance of the Fish
School Search Algorithm, International Journal of Swarm Intelligence Research (IJSIR),
2018, No. 9 (4), pp. 21-46.
12. Lima Neto F.B.D., Lacerda M.G. Weight based fish school search, IEEE International Conference
Systems, Man and Cybernetics (SMC), 2014, pp. 270-277.
13. Bastos-Filho C.J.A., Lima-Neto F.B., Lins Sousa M.F.C., Pontes M.R. On the influence of the
swimming operators in the fish school search algorithm, IEEE International Conference on
Systems Man and Cybernetics, 2009, pp. 5012-5017.
14. Kravchenko Y.A., Bova V.V., Kursitys I.O. The development of genetic algorithm for semantic
similarity estimation in terms of knowledge management problems, Artificial Intelligence Perspectives
in Intelligent Systems. Proceedings of the 6th Computer Science On-line Conference
2017 (CSOC2017), Vol. 1. Springer, 2017, pp. 84-93.
15. Soliman O.S. and Adly A. Bio-inspired algorithm for classification association rules, 8th International
Conference on Informatics and Systems (INFOS), Cairo, 2012, pp. 154-160.
16. Bova V., Zaporozhets D., and Kureichik V. Integration and processing of problem-oriented
knowledge based on evolutionary procedures, Advances in Intelligent Systems and Computing,
2016, Vol. 450, pp. 239-249.
17. Semenova A.V. and Kureichik V.M. Ensemble of classifiers for ontology enrichment, Journal
of Physics: Conference Series, 2018, Vol. 1015, Issue 3, article id. 032123.
18. Kureychik V.M. Overview and problem state of ontology models development, 9th International
Conference on Application of Information and Communication Technologies, AICT
2015 - Proceedings 9, 2015, pp. 558-564.
19. Semenova A.V. and Kureychik V.M. Application of swarm intelligence for domain ontology
alignment, Proceedings of the First International Scientific Conference “Intelligent Information
Technologies for Industry” (IITI’16), 2016, Vol. 1 , pp. 261-270.
20. Bova V., Kureichik V. and Zaruba D. Heuristic approach to model of corporate knowledge
construction in information and analytical systems, 2016 IEEE 10th International Conference
on Application of Information and Communication Technologies (AICT), Baku, 2016, pp. 1-5.
21. Kureichik V., Zaporozhets D., and Zaruba D. Generation of bioinspired search procedures for
optimization problems, Application of Information and Communication Technologies, AICT
2016 - Conference Proceedings, 2016. Vol. 10.
22. Pulyavina N., Taratukhin V. The Future of Project-Based Learning for Engineering and Management
Students: Towards an Advanced Design Thinking Approach, ASEE Annual Conference
and Exposition, Conference Proceedings, 2018, No. 125.
23. Becker J., Pulyavina N., Taratukhin V. Next-Gen Design Thinking. Using Project-Based and
Game-Oriented Approaches to Support Creativity and Innovation, Proceedings of the 1st International
Conference of Information Systems and Design, 2020.
24. Bova V.V., Nuzhnov E.V., Kureichik V.V. The combined method of semantic similarity estimation
of problem oriented knowledge on the basis of evolutionary procedures, Advances in Intelligent
Systems and Computing, 2017, Vol. 573, pp. 74-83.
