Optimizing the parameters of functioning of the system of management of data center it infrastructure

Authors

DOI:

https://doi.org/10.15587/1729-4061.2016.79231

Keywords:

cloud-based services, data center, information criterion, machine learning, swarm algorithm

Abstract

The information-extreme algorithm was developed of machine learning of the management system of a data center for predicting violations of the SLA terms. The scheme of binary encoding of features is considered, where the code of features is determined by the results of control of belonging of its value to the appropriate field of tolerances of each class of recognition. According to the data of tracing the work of virtual machines of a data center, we formed learning samples and synthesized decisive rules, optimal in information sense. The increase in reliability of decisive rules by 8 % is demonstrated, as compared to results of learning by the well-known scheme, where the control tolerances on the attributes' values are defined only for one single base class.

We proposed to use extreme serial statistics in the form of normalized statistics of the numbers of the attributes' values entering their fields of control tolerances for determining the moments of retraining a management system that allows adapting to the change in patterns of consumption of resources of a data center.

The efficiency of additive-multiplicative and entropy convolutions of the partial criteria of quality of functioning of a data center was examined to form the fitness function of swarm algorithm of optimization of the plan to deploy virtual machines of a data center. It is proved by the results of physical modeling that the additive–multiplicative convolution is more efficient on the stage of growth in the load of a data center, while the entropic convolution has highee efficiency during reduction in the load of a data center. In both cases, the decrease in operating expenses of a data center is observed in comparison to the known MBFD algorithm (Modified Best Fit Decreasing). 

Author Biographies

Vyacheslav Moskalenko, Sumy State University Rimsky-Korsakov str., 2, Sumy, Ukraine, 40007

PhD, senior lecturer

Department of Computer Science

Sergey Pimonenko, Sumy State University Rimsky-Korsakov str., 2, Sumy, Ukraine, 40007

Postgraduate student

Department of Computer Science

References

  1. Cao, Z., Dong, S. (2012). Dynamic VM Consolidation for Energy-Aware and SLA Violation Reduction in Cloud Computing. 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies. doi: 10.1109/pdcat.2012.68
  2. Sharma, B. (2009). Applications of Data Mining in the Management of Performance and Power in Data Centers. Technical Report, Department of Computer Science and Engineering, 11–15.
  3. Caglar, F., Shekhar, S., Gokhale, A. (2014). Towards a performance interference-aware virtual machine placement strategy for supporting soft real-time applications in the cloud. Proceedings of the 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications, 15–20.
  4. Delimitrou, C., Kozyrakis, C. (2013). Paragon: QoS-aware scheduling for heterogeneous datacenters. Proceedings of the 18th international conference on Architectural support for programming languages and operating systems, 41, 77–88. doi: 10.1145/2451116.2451125
  5. Hayashi, T., Ohta, S. (2014). Performance Degradation Detection of Virtual Machines Via Passive Measurement and Machine Learning. International Journal of Adaptive, Resilient and Autonomic Systems, 5 (2), 40–56. doi: 10.4018/ijaras.2014040103
  6. Bodik, P., Goldszmidt, M., Fox, A., Woodard, D. B., Andersen, H. (2010). Fingerprinting the datacenter. Proceedings of the 5th European Conference on Computer Systems – EuroSys’10, 111–124. doi: 10.1145/1755913.1755926
  7. Nanduri, R., Maheshwari, N., Reddyraja, A., Varma, V. (2011). Job Aware Scheduling Algorithm for MapReduce Framework. 2011 IEEE Third International Conference on Cloud Computing Technology and Science, 724–729. doi: 10.1109/cloudcom.2011.112
  8. Kandalintsev, A., Lo Cigno, R., Kliazovich, D., Bouvry, P. (2014). Profiling cloud applications with hardware performance counters. The International Conference on Information Networking 2014 (ICOIN2014), 52–57. doi: 10.1109/icoin.2014.6799664
  9. Dovbysh, A. S., Moskalenko, V. V., Rizhova, A. S. (2016). Information-Extreme Method for Classification of Observations with Categorical Attributes. Cybernetics and Systems Analysis, 52 (2), 224–231. doi: /10.1007/s10559-016-9818-1
  10. Dovbysh, A. S., Moskalenko, V. V., Rizhova, A. S. (2016). Learning Decision Making Support System for Control of Nonstationary Technological Process. Journal of Automation and Information Sciences, 48 (6), 39–48. doi: 10.1615/jautomatinfscien.v48.i6.40
  11. Chen, L., Zhang, J., Cai, L., Li, R., He, T., Meng, T. (2015). MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement. International Journal of Distributed Sensor Networks, 2015, 1–14. doi: 10.1155/2015/679170
  12. Salmasnia, A., Bashiri, M. (2014). A new desirability function-based method for correlated multiple response optimization. The International Journal of Advanced Manufacturing Technology, 76 (5-8), 1047–1062. doi: 10.1007/s00170-014-6265-x
  13. Altinoz, O. T., Yilmaz, A. E, Ciuprina, G. (2013). A multiobjective optimization approach via systematical modification of the desirability function shapes. 8th International symposium on advanced topics in electrical engineering, 3–9. doi: 10.1109/atee.2013.6563481
  14. Sanginova, O. (2015). Comparative analysis of some computional schemes for obtaining a compromise solution. Eastern-European Journal of Enterprise Technologies, 1 (4 (73)), 10–18. doi: 10.15587/1729-4061.2015.35607
  15. Shengnan, Z., Jianjun, W. (2015). Multi-response robust design based on improved desirability function. International Conference on Grey Systems and Intelligent Services, 515–520. doi: 10.1109/gsis.2015.7301911
  16. Kushwaha, S., Sikdar, S., Mukherjee, I., Ray, P. K. (2013). A Modified Desirability Function Approach for Mean-Variance Optimization of Multiple Responses. International Journal of Software Science and Computational Intelligence, 5 (3), 7–21. doi: 10.4018/ijssci.2013070101
  17. Yoo, D., Kang, D., Jun, H., Kim, J. (2014). Rehabilitation Priority Determination of Water Pipes Based on Hydraulic Importance. Water, 6 (12), 3864–3887. doi: 10.3390/w6123864
  18. Parpinelli, R. (2012). Theory and New Applications of Swarm Intelligence. InTech. doi: 10.5772/1405
  19. Jain, S. A., Kumar, R., Anamika, Jangir, S. K. (2014). Comparative Study for Cloud Computing Platform on Open Source Software. An International Journal of Engineering & Technolog, 1 (2), 28–34.
  20. Kaur, A., Kalra, M. (2016). Energy optimized VM placement in cloud environment. 2016 6th International Conference – Cloud System and Big Data Engineering (Confluence), 141–145. doi: 10.1109/confluence.2016.7508103

Downloads

Published

2016-10-30

How to Cite

Moskalenko, V., & Pimonenko, S. (2016). Optimizing the parameters of functioning of the system of management of data center it infrastructure. Eastern-European Journal of Enterprise Technologies, 5(2 (83), 21–29. https://doi.org/10.15587/1729-4061.2016.79231