# Correlation-Model-Based Reduction of Monitoring Data in Data Centers

### Xuesong Peng

#### Abstract

Nowadays, in order to observe and control data centers in an optimized way, people collect a variety of monitoring data continuously. Along with the rapid growth of data centers, the increasing size of monitoring data will become an inevitable problem in the future. This paper proposes a correlation-based reduction method for streaming data that derives quantitative formulas between correlated indicators, and reduces the sampling rate of some indicators by replacing them with formulas predictions. This approach also revises formulas through iterations of reduction process to find an adaptive solution in dynamic environments of data centers. One highlight of this work is the ability to work on upstream side, i.e., it can reduce volume requirements for data collection of monitoring systems. This work also carried out simulated experiments, showing that our approach is capable of data reduction under typical workload patterns and in complex data centers.

#### References

- Carvalho, C., Gomes, D. G., Agoulmine, N., and De Souza, J. N. (2011). Improving prediction accuracy for wsn data reduction by applying multivariate spatiotemporal correlation. Sensors, 11(11):10010-10037.
- Ding, R., Wang, Q., Dang, Y., Fu, Q., Zhang, H., and Zhang, D. (2015). Yading: fast clustering of largescale time series data. Proceedings of the VLDB Endowment, 8(5):473-484.
- Esling, P. and Agon, C. (2012). Time-series data mining. ACM Computing Surveys (CSUR), 45(1):12.
- Hayashi, F. (2000). Econometrics. Princeton Univ. Press, Princeton, NJ [u.a.].
- Jolliffe, I. (2002). Principal component analysis. Wiley Online Library.
- Keogh, E., Chakrabarti, K., Pazzani, M., and Mehrotra, S. (2001). Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems, 3(3):263-286.
- Kung, H., Lin, C.-K., and Vlah, D. (2011). Cloudsense: Continuous fine-grain cloud monitoring with compressive sensing. In HotCloud.
- Peng, X. (2015). Data reduction in monitored data. In Loucopoulos, P., Nurcan, S., and Weigand, H., editors, Proceedings of the CAiSE'2015 Doctoral Consortium at the 27th International Conference on Advanced Information Systems Engineering (CAiSE 2015), Stockholm, Sweden, June 11-12, 2015., volume 1415 of CEUR Workshop Proceedings, pages 39-46. CEURWS.org.
- Reeves, G., Liu, J., Nath, S., and Zhao, F. (2009). Managing massive time series streams with multi-scale compressed trickles. Proceedings of the VLDB Endowment, 2(1):97-108.
- Tsamardinos, I., Brown, L. E., and Aliferis, C. F. (2006). The max-min hill-climbing bayesian network structure learning algorithm. Machine learning, 65(1):31- 78.
- Vitali, M., O'Reilly, U.-M., and Veeramachaneni, K. (2013). Modeling service execution on data centers for energy efficiency and quality of service monitoring. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pages 103-108. IEEE.
- Vitali, M., Pernici, B., and OReilly, U.-M. (2015). Learning a goal-oriented model for energy efficient adaptive applications in data centers. Information Sciences, 319:152-170.
- Zhou, S., Lin, K.-J., Na, J., Chuang, C.-C., and Shih, C.-S. (2015). Supporting service adaptation in fault tolerant internet of things. In Service-Oriented Computing and Applications (SOCA), 2015 IEEE 8th International Conference on, pages 65-72.

#### Paper Citation

#### in Harvard Style

Peng X. and Pernici B. (2016). **Correlation-Model-Based Reduction of Monitoring Data in Data Centers** . In *Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,* ISBN 978-989-758-184-7, pages 395-405. DOI: 10.5220/0005794803950405

#### in Bibtex Style

@conference{smartgreens16,

author={Xuesong Peng and Barbara Pernici},

title={Correlation-Model-Based Reduction of Monitoring Data in Data Centers},

booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},

year={2016},

pages={395-405},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005794803950405},

isbn={978-989-758-184-7},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,

TI - Correlation-Model-Based Reduction of Monitoring Data in Data Centers

SN - 978-989-758-184-7

AU - Peng X.

AU - Pernici B.

PY - 2016

SP - 395

EP - 405

DO - 10.5220/0005794803950405