Data storage management using AI methods

• Autorzy: Funika W. , Szura P.
• Języki publikacji: EN

Abstrakty

EN

Data management and monitoring is an important issue in scientific computation. Scientists want to access their data as quickly as possible. Some experiments need to store a lot of data which have to be secure. By saying this we mean that this data can not disappear or be damaged also the data storage should be as cheap as possible. In this paper we present an approach to the automation of monitoring and management of data storage. We introduce a knowledge based system which is able to manage data, i.e., make decisions on migrating data, replicating or removing it. We discuss some of the existing solutions which are popular on the market. In this paper we aim to present our system which uses such AI techniques like fuzzy logic or a rule-based expert system to deal with data storage management. We exploit in this system a cost model to analyze the proposed solutions. The operations performed by our system are aimed to optimize the usage of the monitored infrastructure.

Słowa kluczowe

EN

data management; AI; fuzzy logic; rules; data storage

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2013
• Tom: Vol. 14 (2)
• Strony: 177--190
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Funika W.

• AGH University of Science and Technology, Krakow, Poland

autor

Szura P.

• University of Science and Technology, ACC CYFRONET AGH, Krakow, Poland

Bibliografia

• [1] Drools expert user guide. http://downloads.jboss.com/drools/docs/5.1.1. 34858.FINAL/drools-expert/html/index.html
• [2] Nagios core 3.x documentation. http://nagios.sourceforge.net/docs/nagioscore/3/en/toc.html
• [3] Zabbix 1.8 documentation. http://www.zabbix.com/documentation/1.8/manual
• [4] Abdurrab A. R., Xie T.: FIRE: A file reunion based data replication strategy for data grids. In Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on , pp. 215–223, May 2010
• [5] Ben Charrada F., Ounelli H., Chettaoui H.: An efficient replication strategy for dynamic data grids. In P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2010 International Conference on, pp. 50–54, Nov. 2010
• [6] de Franceschi A., da Rocha M., Weber H., Westphall C.: Proactive network management using remote monitoring and artificial intelligence techniques. Com- 188 Włodzimier zFunika, Filip Szura puters and Communications, 1997. Proceedings., Second IEEE Symposium on, pp. 167–171, July 1997
• [7] Funika W., Korcyl K., Pieczykolan J., Skital L., Balos K., Slota R., Guzy K., Dutka L., Kitowski J., Zielinski K.: Adapting a hep application for running on the grid. Computing and Informatics, vol. 28, pp. 353–367, 2009
• [8] Hellmann M.: Fuzzy logic introduction. http://diuf.unifr.ch/ds/courses/ dss2002/pdf/FuzzyLogic.pdf
• [9] Krol D., Funika W., Slota R., Kitowski J.: SLA-oriented semi-automatic management of data storage and applications in distributed environment. Computer Science – Annual AGH-UST, vol. 11, pp. 37–50, 2010
• [10] Krol D., Kryza B., Skalkowski K., Nikolow D., Slota R., Kitowski J.: QoS provisioning for data-oriented applications in pl-grid. Cracow Grid Workshop – CGW’10, pp. 142–150. ACC-Cyfronet AGH, October 2010
• [11] Lamehamedi H., Shentu Z., Szymanski B., Deelman E.: Simulation of dynamic data replication strategies in data grids. In Parallel and Distributed Processing Symposium, 2003. Proceedings. International, pp. 75–86. IEEE CS Press, April 2003.
• [12] Prodan R., Nae V.: Prediction-based real-time resource provisioning for mas- sively multiplayer online games. Future Generation Computer Systems, 25(7): 785–793, 2009.
• [13] Ranganathan K., Foster I. T.: Identifying dynamic replication strategies for a high-performance data grid. In Proceedings of the Second International Workshop on Grid Computing, GRID ’01, pp. 75–86, London, UK, UK, 2001. Springer-Verlag
• [14] Ribler R. L., Simitci H., Reed D. A.: The autopilot performance-directed adaptive control system. Future Gener. Comput. Syst, 18(1):175–187, September 2001.
• [15] Sota R., Krl D., Skakowski K., Orzechowski M., Nikolow D., Kryza B., Wrzeszcz M., Kitowski J.: A toolkit for storage qos provisioning for data-intensive applications. Computer Science, vol. 13(1): 63–73, 2012.
• [16] Wang X., Yang G., Li Y., Liu D.: Review on the application of artificial intelligence in antivirus detection systems. In Cybernetics and Intelligent Systems, 2008 IEEE Conference on, pp. 506–509, September 2008.
• [17] Wismueller R., Bubak M., Funika W., Arodz T., Kurdziel M.: Support for user- defined metrics in the online performance analysis tool G-PM. In Dikaiakos M. D., editor, Grid Computing European Across Grids Conference AxGrids’04, volume 3165 of Lecture Notes in Computer Science, pp. 159–168. Springer, January 2004.