Abstract
This paper presents a multi-layer monitoring and analysis approach for Cloud computing environments based on the methodology of Complex Event Processing (CEP). Instead of having to manually specify continuous queries on monitored event streams, CEP queries are derived from analyzing the correlations between monitored metrics across multiple Cloud layers. The results of our correlation analysis allow us to reduce the number of monitored parameters and enable us to perform a root cause analysis to identify the causes of performance-related problems. The derived analysis rules are implemented as queries in a CEP engine. The results of several experiments demonstrate the benefits of the proposed approach in terms of precision and recall in comparison with threshold-based methods. They also show the accuracy of our approach in identifying the causes of performance-related problems.
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Notes
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A data point represents one measurement of the studied metric.
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A complete version of our fishbone diagram can be found at http://www.redcad.org/members/mdhaffar/cep4cma/Fishbone.html.
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Sysbench is a multi-threaded benchmark tool. It allows us to evaluate OS parameters by injecting different kinds of load: http://sysbench.sourceforge.net/docs/.
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References
Kutare, M., Eisenhauer, G., Wang, C., Schwan, K., Talwar, V., Wolf, M.: Monalytics: online monitoring and analytics for managing large scale data centers. In: Proceedings of the 7th International Conference on Autonomic Computing, pp. 141–150. ACM (2010)
De Chaves, S.A., Uriarte, R.B., Westphall, C.B.: Toward an architecture for monitoring private clouds. IEEE Commun. Mag. 49, 130–137 (2011)
Yigitbasi, N., Iosup, A., Epema, D., Ostermann, S.: C-meter: a framework for performance analysis of computing clouds. In: Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 472–477. IEEE (2009)
Dos Teixeira, P.H.S., Clemente, R.G., Kaiser, R.A., Vieira Jr., D.A.: HOLMES: an event-driven solution to monitor data centers through continuous queries and machine learning. In: Proceedings of the 4th ACM International Conference On Distributed Event-Based Systems, pp. 216–221. ACM (2010)
Sarkar, S., Mahindru, R., Hosn, R.A., Vogl, N., Ramasamy, H.V.: Automated incident management for a platform-as-a-service cloud. In: Proceedings of the 11th USENIX Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services, USENIX Association, pp. 1–6 (2011)
Mi, H., Wang, H., Yin, G., Cai, H., Zhou, Q., Sun, T., Zhou, Y.: Magnifier: online detection of performance problems in large-scale cloud computing systems. In: Proceedings of the 11th IEEE International Conference on Services Computing, pp. 418–425. IEEE (2011)
Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 1–62 (2012)
Bhaumik, S.: Root cause analysis in engineering failures. Trans. Indian Inst. Met. 63, 297–299 (2010)
Cohen, I., Goldszmidt, M., Kelly, T., Symons, J.: Correlating instrumentation data to system states: a building block for automated diagnosis and control. In: Proceedings of the 6th Symposium on Operating Systems Design and Implementation, pp. 231–244 (2004)
Wang, C., Talwar, V., Schwan, K., Ranganathan, P.: Online detection of utility cloud anomalies using metric distributions. In: 12th IEEE/IFIP Network Operations and Management Symposium, pp. 96–103. IEEE (2010)
Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30, 817–840 (2004)
Mdhaffar, A., Halima, R.B., Juhnke, E., Jmaiel, M., Freisleben, B.: AOP4CSM: an aspect-oriented programming approach for cloud service monitoring. In: Proceedings of the 11th IEEE International Conference on Computer and Information Technology, pp. 363–370. IEEE Press (2011)
Rabkin, A.: Chukwa: a large-scale monitoring system. In: Cloud Computing and its Applications, pp. 1–5 (2008)
Gupta, D., Gardner, R., Cherkasova, L.: XenMon: QoS monitoring and performance profiling tool. Technical report, HP Labs (2005)
Nance, K.L., Bishop, M., Hay, B.: Virtual machine introspection: observation or interference? IEEE Secur. Priv. 6(5), 32–37 (2008)
Taylor, R.: Interpretation of the correlation coefficient: a basic review. J. Diagn. Med. Sonogr. 6, 35–39 (1990)
Crocker, D.C.: Some interpretations of the multiple correlation coefficient. Am. Stat. 26, 31–33 (1972)
Faul, F., Erdfelder, E., Buchner, A., Lang, A.G.: Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41, 1149–1160 (2009)
von Hagen, W.: Professional Xen Virtualization. Wiley, Indianapolis (2008)
Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 1–42 (2010)
Acknowledgments
This work is partly supported by the German Ministry of Education and Research (BMBF) and the German Academic Exchange Service (DAAD).
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Mdhaffar, A., Halima, R.B., Jmaiel, M., Freisleben, B. (2014). CEP4CMA: Multi-layer Cloud Performance Monitoring and Analysis via Complex Event Processing. In: Noubir, G., Raynal, M. (eds) Networked Systems. NETYS 2014. Lecture Notes in Computer Science(), vol 8593. Springer, Cham. https://doi.org/10.1007/978-3-319-09581-3_10
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