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Predictive Maintenance Proposal for Server Infrastructures

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Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference (DCAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1242))

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Abstract

Predictive maintenance has played a key role in industry complexes for several years. The prediction of upcoming errors has the potential to increase the average productivity of any enterprise and to avoid losing opportunities due to a partial shut-down of the production system. Such ideas can be applied to large-scale server infrastructures achieving a fully automatic system which warns the administrators of a potential shut-down before it takes place. Mathematical algorithms and statistical tools can be used to model the standard behaviour of the system and, when an anomaly is detected, to warn the system operator. Furthermore, machine learning can also be used to model such a behaviour and to identify the most likely cause of the anomaly.

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References

  1. López, M., Pedraza, J., Carbó, J., Molina, J.M.: The awareness of privacy issues in ambient intelligence. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 3(2), 71–84 (2014). ISSN 2255-2863

    Article  Google Scholar 

  2. Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: 17th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE, July 2014

    Google Scholar 

  3. Bullon, J., et al.: Manufacturing processes in the textile industry. Expert systems for fabrics production. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 6(4), 15–23 (2017)

    Article  Google Scholar 

  4. Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Méndez, J.R., Corchado, J.M.: Applying lazy learning algorithms to tackle concept drift in spam filtering. Expert Syst. Appl. 33(1), 36–48 (2007)

    Article  Google Scholar 

  5. Souza de Castro, L.F., Alves, G.V., Borges, A.P.: Using trust degree for agents in order to assign spots in a Smart Parking (2017)

    Google Scholar 

  6. Moung, E.: A comparison of the YCBCR color space with gray scale for face recognition for surveillance applications. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 6(4), 25–33 (2017)

    Article  Google Scholar 

  7. Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl.-Based Syst. 137, 54–64 (2017)

    Article  Google Scholar 

  8. Kethareswaran, V., Sankar Ram, C.: An Indian perspective on the adverse impact of Internet of Things (IoT). ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 6(4), 35–40 (2017)

    Article  Google Scholar 

  9. Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016)

    Article  Google Scholar 

  10. Cunha, R., Billa, C., Adamatti, D.: Development of a graphical tool to integrate the prometheus AEOlus methodology and Jason platform. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 6(2), 57–70 (2017)

    Article  Google Scholar 

  11. Coria, J.A.G., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4), 1189–1205 (2014)

    Article  Google Scholar 

  12. Siyau, M.F., Li, T., Loo, J.: A novel pilot expansion approach for MIMO channel estimation. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 3(3), 12–20 (2014). ISSN 2255-2863

    Article  Google Scholar 

  13. Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems. Inf. Sci. 222, 47–65 (2013)

    Article  Google Scholar 

  14. García-Retuerta, D., Bartolomé, Á., Chamoso, P., Corchado, J.M.: Counter-terrorism video analysis using hash-based algorithms. Algorithms 12(5), 110 (2019)

    Article  Google Scholar 

  15. Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: European Conference on Case-Based Reasoning, pp. 547–559. Springer, Heidelberg, August 2004

    Google Scholar 

  16. Lima, A.C.E., de Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015)

    MATH  Google Scholar 

  17. Fdez-Riverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004)

    Article  Google Scholar 

  18. Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Méndez, J.R., Corchado, J.M.: SpamHunting: an instance-based reasoning system for spam labelling and filtering. Decis. Supp. Syst. 43(3), 722–736 (2007)

    Article  Google Scholar 

  19. Casado-Vara, R., Martin-del Rey, A., Affes, S., Prieto, J., Corchado, J.M.: IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future Gen. Comput. Syst. 102, 965–977 (2020)

    Article  Google Scholar 

  20. Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010)

    Article  Google Scholar 

  21. Casado-Vara, R., Prieto, J., De la Prieta, F., Corchado, J.M.: How blockchain improves the supply chain: case study alimentary supply chain. Procedia Comput. Sci. 134, 393–398 (2018)

    Article  Google Scholar 

  22. Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 32(4), 307–313 (2002)

    Article  Google Scholar 

  23. González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.M.: Energy optimization using a case-based reasoning strategy. Sensors 18(3), 865 (2018)

    Article  Google Scholar 

  24. Díaz, F., Fdez-Riverola, F., Corchado, J.M.: gene-CBR: a case: based reasonig tool for cancer diagnosis using microarray data sets. Comput. Intell. 22(3–4), 254–268 (2006)

    Article  Google Scholar 

  25. Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. In: International Conference on Case-Based Reasoning, pp. 107–121. Springer, Heidelberg, June 2003

    Google Scholar 

  26. Bartolomé, Á., García-Retuerta, D., Pinto-Santos, F., Chamoso, P.: Internet data extraction and analysis for profile generation. In: International Symposium on Ambient Intelligence, pp. 112–119. Springer, Cham, June 2019

    Google Scholar 

  27. Ribeiro, C., et al.: Customized normalization clustering meth-odology for consumers with heterogeneous characteristics. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 7(2), 53–69 (2018)

    Article  Google Scholar 

  28. Guillén, J.H., del Rey, A.M., Casado-Vara, R.: Security countermeasures of a SCIRAS model for advanced malware propagation. IEEE Access 7, 135472–135478 (2019)

    Article  Google Scholar 

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Acknowledgements

This paper has been partially supported by the Salamanca Ciudad de Cultura y Saberes Foundation under the Talent Attraction Programme (CHROMOSOME project).

Author information

Authors and Affiliations

  1. University of Salamanca, Patio de Escuelas Menores, 37008, Salamanca, Spain

    David García-Retuerta

Authors
  1. David García-Retuerta
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Correspondence to David García-Retuerta .

Editor information

Editors and Affiliations

  1. IoT European Digital Innovation Hub, Bioinformatics Intelligent Systems and Educational Technology Research Group, Department of Computer Science, Faculty of Science, University of Salamanca, Salamanca, Spain

    Sara Rodríguez González

  2. GRASIA Research Group, Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Alfonso González-Briones

  3. Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Lublin University of Technology, Lublin, Poland

    Arkadiusz Gola

  4. Cerca Trova Ltd, Sofia, Bulgaria

    George Katranas

  5. Department of Biology, Ecology and Earth Science, University of Calabria, Arcavacata di Rende, Cosenza, Italy

    Michela Ricca

  6. Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia, Bulgaria

    Roussanka Loukanova

  7. Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Salamanca, Spain

    Javier Prieto

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García-Retuerta, D. (2021). Predictive Maintenance Proposal for Server Infrastructures. In: Rodríguez González, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference. DCAI 2020. Advances in Intelligent Systems and Computing, vol 1242. Springer, Cham. https://doi.org/10.1007/978-3-030-53829-3_30

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