Abstract
The current paper deals with the problem of workload relocation in distributed monitoring and forecasting systems in terms of their reliability and system response. The research of publications presented allows us to conclude that existing methods of workload relocation problem-solving do not ensure getting of a satisfying result. Also, some specific characteristics of such systems are not incorporated in known methods. In this paper the modification of the existing workload relocation problem-solving method was proposed. In this context, a new domain ontological model and a new set of production rules were developed to adopt the method, which was proposed for distributed CAD systems. Some simulations for two different scenarios were conducted and theoretical conclusions were confirmed. A particular interest is the fact that the higher fog “deepness” value, the higher the effectiveness of developed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Puchkov V.A.: Modern systems for monitoring and forecasting emergency. FSI Center for Strategic Research of Civil Protection of the Ministry of Emergency Situations of the Russian Federation, Moscow (2013)
Vorobyev, Yu.L.: National security and strategic risk management in Russia. Risk Manage. (Special issue), 4–9 (2002)
Melnik, E.V., Orda-Zhigulina, M.V., Orda-Zhigulina, D.V., Rodina, A.A.: Reliability method by reconfiguration resources in monitoring and diagnostic systems of hazardous phenomena. Izvestiya Tula State Univ. 2, 18–26 (2020)
Orda-Zhigulina, M.V., Melnik, E.V., Ivanov, D.Ya., Rodina, A.A.: Combined method of monitoring and predicting of hazardous phenomena. Adv. Intell. Syst. Comput. 984, 55–61 (2019)
Qiao, H., Wang, T., Wang, P.: A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing. Int. J. Adv. Manuf. Technol. 108(7–8), 2367–2384 (2020). https://doi.org/10.1007/s00170-020-05548-8
Melnik, E.V., Klimenko, A.B., Ivanov, D.Ya.: A model of device group forming of informational control systems in the fog-computing environment. In: XIII All-Russian Meeting on Control Problems 2019 Proceedings, pp. 2979–2984. Institute of Control Sciences of Russian Academy of Sciences, Moscow (2019)
Kureychik, V., Safronenkova, I.: Efficiency enhancement method for distributed CAD systems in fog-computing environments. J. Phys.: Conf. Ser. 1333, 082006 (2019)
Protégé. https://protege.stanford.edu. Accessed 03 Dec 2020
Ontology Development 101: A Guide to Creating Your First Ontology. https://protege.stanford.edu/publications/ontology_development/ontology101.pdf. Accessed 03 Dec 2020
Machine Learning Classifiers. https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623. Accessed 03 Dec 2020
Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: Proceedings of 2014 Federated Conference on Computer Science and Information Systems, vol. 2, pp. 1–8 (2014)
Cisco Fog Computing Solutions: Unleash the Power of the Internet of Things. https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-solutions.pdf. Accessed 03 Dec 2020
Safronenkova, I.B., Melnik, E.V., Kureychik, V.M.: Certificate of state registration of a computer program 2020616523 Russian Federation. A module of ontological analysis for distributed CAD system (2020)
Acknowledgements
This study is supported by the by the RFBR project 18-05-80092, 18-29-03229 and the GZ SSC RAS N GR project AAAA-A19–119011190173-6.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Melnik, E.V., Safronenkova, I.B., Klimenko, A.B. (2021). A Modified Ontology-Based Method of Workload Relocation Problem Solving for Monitoring and Forecasting Systems. In: Silhavy, R. (eds) Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-030-77445-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-77445-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77444-8
Online ISBN: 978-3-030-77445-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)