DOI: 10.17586/2226-1494-2018-18-5-858-862


INFORMATION ON UTILIZATION OF DATA CENTER RESOURCES WITH MESSAGE BROKER IMPLEMENTATION

N. Y. Samokhin, S. E. Khoruzhnikov, V. M. Trubnikova, R. R. Akhmedzyanova, A. B. Bulykina


Read the full article 
Article in English

For citation: Samokhin N.Yu., Khoruzhnikov S.E., Trubnikova V.M., Akhmedzyanova R.R., Bulykina A.B. Information on utilization of data center resources with message broker implementation. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 5, pp. 858–862 (in Russian). doi: 10.17586/2226-1494-2018-18-5-858-862

Abstract

 A cloud of geographically distributed data centers developed by ITMO University (ifmo.ru) is presented.To increase reliability, as well as to collect statistical data, a special monitoring system is created that monitors the state of systems and the utilization of hardware resources. In addition to building a map of statistical data, information on resource utilization is suggested for the provider to perform a billing. An approach to monitoring system with asynchronous communication is proposed. An option with message broker implementation is shown. Out of two basic billing models, billing by quotas and billing by actual usage, we propose a model for organizing a dialogue between the administrator and the monitoring system in order to obtain data on resource utilization by actual usage. The use of a database for storing requests and responses is proposed, as well as the asynchronous dialogue using queues technology and message broker. Approbation was carried out on testing equipment with an actual infrastructure simulation. Unique asynchronous multi-agent system was obtained for statistics gathering on data center resources utilization. RabbitMQ message broker, PostgreSQL database and Zabbix monitoring system implementations are shown. The dialogue system was optimized with the help of Python code. The designed model can be recommended for implementation aimed at utilization of data obtained in different data centers, including distributed ones. Suggested model can be used in high-loaded systems  providing virtual resources as a service.


Keywords: data center, virtualization, monitoring, billing, message broker, Zabbix, RabbitMQ

Acknowledgements. The research was carried out with the financial support of the Ministry of Education and Science of the Russian Federation (Contract No. 03.G25.31.0229).

References
  1. Shevel A.Y., Khoruzhnikov S.E., Grudinin V.A., Sadov O.L., Kairkanov A. Globally distributed software defined storage (proposal).  Journal of Physics: Conference Series, 2017, vol. 898, no. 6, art. 062014. doi: 10.1088/1742-6596/898/6/062014
  2. Fedchenkov P.V., Khoruzhnikov S.E., Grudinin V.A., Sadov O.L., Shevel A.E., Kairkanov A.B., Lazo O.I., Oreshkin A.A. Service reliability in the cloud of data centers under Openstack. CEUR Workshop Proceedings, 2017, vol. 2023, pp. 282–287.
  3. Shevel A.Y., Khoruzhnikov S.E., Grudinin V.A., Sadov O.L., Kairkanov A.B. Geographically distributed software defined storage (the proposal). CEUR Workshop Proceedings, 2016, vol. 1787, pp. 62–67.
  4. Khoruzhnikov S.E., Grudinin V.A., Sadov O.L., Shevel A.E., Kairkanov A.B. Transfer of large volume data over Internet with parallel data links and SDN. Lecture Notes in Computer Science, 2015, vol. 9142, pp. 463–471. doi: 10.1007/978-3-319-20469-7_49
  5. Khoruzhnikov S.E., Grudinin V.A., Sadov O.L., Shevel A.E., Titov V.B., Kairkanov A.B. Initial-stage examination of a testbed for the big data transfer over parallel links. The SDN approach. Astrophysical Bulletin, 2015, vol. 70, no. 2, pp. 238–242. doi: 10.1134/S199034131502011X
  6. Chen Y., Paxson V., Katz R. What’s New about Cloud Computing Security? Technical Report UCB/EECS-2010-5. Berkeley, 2009.
  7. Greenberg A., Hamilton J.R., Jain N., Kandula S., Kim C., Lahiri P., Maltz D.A., Patel P., Sengupta S.VL2: scalable and flexible data center network. Computer Communication Review, 2009, vol. 39, no. 4, pp. 51–62. doi: 10.1145/1594977.1592576
  8. Mohd Saleem. Cloud computing virtualization. International Journal of Computer Applications Technology and Research, 2017, vol. 6, no. 7, pp. 290–292.
  9. Armbrust M., Fox A., Griffith R., Joseph A.D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., Zaharia M. A view of cloud computing. Communications of the ACM, 2010, vol. 53, no. 4, pp. 50–58. doi: 10.1145/1721654.1721672
  10. Ali-Eldin A.H. Capacity Scaling for Elastic Compute Clouds. Dissertation. Umea University, Sweden, 2013, 90 p.
  11. Liu S.Y. Research and implementation of automated inspection system based on ZABBIX. Electric Power Information and Communication Technology, 2014, vol. 12, no. 12, pp. 111–115.
  12. Dalle Vacche A. Mastering Zabbix. 2nd ed. Packt Publ., 2015, 412 p.
  13. Elmasri R., Navathe S.B. Fundamentals of Database Systems. 6th ed. Addison Wesley, 2010, 1200 p.
  14. Momjian B. PostgreSQL: Introduction and Concepts. Addison-Wesley, 2002, 490 p.
  15. Dierbach C. Introduction to Computer Science Using Python: A Computational Problem-Solving Focus. Wiley, 2012, 612 p.
  16. Millman K.J., Aivazis M. Python for scientists and engineers. Computing in Science and Engineering, 2011, vol. 13, no. 2, pp. 9–13. doi: 10.1109/MCSE.2011.36
  17. Rostanski M., Grochla K., Seman A. Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ. Proc. 2014 Federated Conference on Computer Science and Information Systems. Warsaw, Poland, 2014, vol. 2, pp. 879–884. doi: 10.15439/2014F48


Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2019 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.

Яндекс.Метрика