Reducing Monitoring Overhead in Virtualized Environments Through Feature Selection
Resumo
Cloud computing has emerged as a cost-effective paradigm for hosting and delivering services. Cloud providers adopt server consolidation strategies to achieve efficient management of resources. A drawback is that applications running on the same host compete for physical resources. Such interference can affect the performance of applications. Performance monitors are useful tools to detect or even predict performance degradation. However, the monitoring itself can be a source of contention. In this paper, we analyze the influence of performance monitoring overhead in virtualized environments. Furthermore, as a mean to reduce contention for shared resources, we propose an approach to reduce the dimensionality of the performance feature space.
Referências
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Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A. (2015). Determining the Best Number of Clusters in a Data Set. https://cran.rproject.org/web/packages/NbClust/NbClust.pdf, 3,0 edition.
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Popiolek, P. and Mendizabal, O. (2012). Monitoring and analysis of performance impact in virtualized environments. Journal of Applied Computing Research, 2(2):75–82.
Pu, X., Liu, L., Mei, Y., Sivathanu, S., Koh, Y., and Pu, C. (2010). Understanding performance interference of i/o workload in virtualized cloud environments. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, pages 51–58. IEEE.
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Rameshan, N., Navarro, L., Monte, E., and Vlassov, V. (2014). Stay-away, protecting sensitive applications from performance interference. In Proceedings of the 15th International Middleware Conference, pages 301–312. ACM.
Shang, W., Hassan, A. E., Nasser, M., and Flora, P. (2015). Automated detection of performance regressions using regression models on clustered performance counters. In Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, pages 15–26. ACM.
Shirvani, M. H. and Ghojoghi, A. (2016). Server consolidation schemes in cloud computing environment: a review. European Journal of Engineering Research and Science, 1(3):18–24.
Taylor, R. (1990). Interpretation of the correlation coefcient: a basic review. Journal of diagnostic medical sonography, 6(1):35–39.
Yu, L. and Liu, H. (2004). Efcient feature selection via analysis of relevance and redundancy. Journal of machine learning research, 5(Oct):1205–1224.
Zhang, J. and Figueiredo, R. J. (2006). Application classication through monitoring and learning of resource consumption patterns. In Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, pages 10–pp. IEEE.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1):7–18.
Alves, M. M. and Drummond, L. M. d. A. (2016). A quantitative model for arXiv preprint predicting cross-application interference in virtual environments. arXiv:1610.04309.
Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A. (2015). Determining the Best Number of Clusters in a Data Set. https://cran.rproject.org/web/packages/NbClust/NbClust.pdf, 3,0 edition.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11):27–34.
Fedorova, A., Blagodurov, S., and Zhuravlev, S. (2010). Managing contention for shared resources on multicore processors. Communications of the ACM, 53(2):49–57.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edition.
Jin, H., Qin, H., Wu, S., and Guo, X. (2015). Ccap: a cache contention-aware virtual International Journal of Parallel Promachine placement approach for hpc cloud. gramming, 43(3):403–420.
Mars, J., Tang, L., Hundt, R., Skadron, K., and Soffa, M. L. (2011). Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations. In Proceedings of the 44th annual IEEE/ACM International Symposium on Microarchitecture, pages 248–259. ACM.
McDougall, R. and Anderson, J. (2010). Virtualization performance: perspectives and challenges ahead. ACM SIGOPS Operating Systems Review, 44(4):40–56.
Mell, P. and Grance, T. (2011). The nist denition of cloud computing. Retrieved from http://csrc. nist. gov/publications/nistpubs/800-145/SP800-145.pdf.
Microsoft. Windows reliability and performance monitor. https://technet.microsoft.com/en-us/library/cc749249(v=ws.10).aspx. Last accessed: Jun-2017.
Popiolek, P. and Mendizabal, O. (2012). Monitoring and analysis of performance impact in virtualized environments. Journal of Applied Computing Research, 2(2):75–82.
Pu, X., Liu, L., Mei, Y., Sivathanu, S., Koh, Y., and Pu, C. (2010). Understanding performance interference of i/o workload in virtualized cloud environments. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, pages 51–58. IEEE.
Rameshan, N. (2016). On the role of performance interference in consolidated environments. In IEEE/USENIX International Conference on Autonomic Computing (ICAC). KTH Royal Institute of Technology.
Rameshan, N., Navarro, L., Monte, E., and Vlassov, V. (2014). Stay-away, protecting sensitive applications from performance interference. In Proceedings of the 15th International Middleware Conference, pages 301–312. ACM.
Shang, W., Hassan, A. E., Nasser, M., and Flora, P. (2015). Automated detection of performance regressions using regression models on clustered performance counters. In Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, pages 15–26. ACM.
Shirvani, M. H. and Ghojoghi, A. (2016). Server consolidation schemes in cloud computing environment: a review. European Journal of Engineering Research and Science, 1(3):18–24.
Taylor, R. (1990). Interpretation of the correlation coefcient: a basic review. Journal of diagnostic medical sonography, 6(1):35–39.
Yu, L. and Liu, H. (2004). Efcient feature selection via analysis of relevance and redundancy. Journal of machine learning research, 5(Oct):1205–1224.
Zhang, J. and Figueiredo, R. J. (2006). Application classication through monitoring and learning of resource consumption patterns. In Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, pages 10–pp. IEEE.
Zhang, Q., Cheng, L., and Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1):7–18.
Publicado
10/05/2018
Como Citar
POPIOLEK, Pedro F.; MACHADO, Karina S.; MENDIZABAL, Odorico M..
Reducing Monitoring Overhead in Virtualized Environments Through Feature Selection. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 15-28.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2018.2403.
