Accurate Analysis and Prediction of Enterprise Service-Level Performance

Abstract

An enterprise service-level performance time series is a sequence of data points that quantify demand, throughput, average order-delivery time, quality of service, or end-to-end cost. Analytical and predictive models of such time series can be embedded into an enterprise information system (EIS) in order to provide meaningful insights into potential business problems and generate guidance for appropriate solutions. Time-series analysis includes periodicity detection, decomposition, and correlation analysis. Time-series prediction can be modeled as a regression problem to forecast a sequence of future time-series datapoints based on the given time series. The state-of-the-art (baseline) methods employed in time-series prediction generally apply advanced machine-learning algorithms. In this article, we propose a new univariate method for dealing with midterm time-series prediction. The proposed method first analyzes the hierarchical periodic structure in one time series and decomposes it into trend, season, and noise components. By discarding the noise component, the proposed method only focuses on predicting repetitive season and smoothed trend components. As a result, this method significantly improves upon the performance of baseline methods in midterm time-series prediction. Moreover, we propose a new multivariate method for dealing with short-term time-series prediction. The proposed method utilizes cross-correlation information derived from multiple time series. The amount of data taken from each time series for training the regression model is determined by results from hierarchical cross-correlation analysis. Such a data-filtering strategy leads to improved algorithm efficiency and prediction accuracy. By combining statistical methods with advanced machine-learning algorithms, we have achieved a significantly superior performance in both short-term and midterm time-series predictions compared to state-of-the-art (baseline) methods.

References

1. A. Biem, H. Feng, A. V. Riabov, and D. S. Turaga. 2013. Real-time analysis and management of big time-series data. IBM J. Res. Develop. 57, 3/4 (2013), 1--8.
2. S. Biswas, H. F. Wang, and R. D. Blanton. 2014. Reducing test cost of integrated, heterogeneous systems using pass-fail test data analysis. ACM Trans. Des. Autom. Electron. Syst. 19, 2, Article 20 (2014), 23 pages.
3. Tim Bollerslev. 1986. Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31, 3 (1986), 307--327.
4. G. E. P. Box, G. Jenkins, and G. Reinsel. 1994. Time Series Analysis: Forecasting and Control (3rd ed.). Prentice Hall, Upper Saddle River, NJ.
5. L. J. Cao and F. E. H. Tay. 2003. Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14, 6 (2003), 1506--1518.
6. C. Chatfield. 2013. The Analysis of Time Series: An Introduction. CRC Press.
7. Ludmila Cherkasova and Minaxi Gupta. 2002. Characterizing locality, evolution, and life span of accesses in enterprise media server workloads. In Proceedings of the 12th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV'02). ACM, 33--42.
8. R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning. 1990. STL: A seasonal-trend decomposition procedure based on loess. J. Official Stat. 61 (1990), 3--73.
9. R. Cochran and S. Reda. 2013. Thermal prediction and adaptive control through workload phase detection. ACM Trans. Des. Autom. Electron. Syst. 18, 1, Article 7 (2013), 19 pages.
10. J. W. Cooley and J. W. Tukey. 1965. An algorithm for the machine computation of complex Fourier series. Math. Comput. 19, 90 (1965), 297--301.
11. N. de Freitas, M. Milo, P. Clarkson, M. Niranjan, and A. Gee. 1999. Sequential support vector machines. In Neural Networks for Signal Processing IX: Proceedings of the IEEE Signal Processing Society Workshop. 31--40.
12. Thomas G. Dietterich. 2002. Machine learning for sequential data: A review. In Proceedings of the Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, vol. 2396, Springer, Berlin Heidelberg, 15--30.
13. C. L. Dunn, J. O. Cherrington, A. S. Hollander, and E. L. Denna. 2005. Enterprise Information Systems: A Pattern-Based Approach. Vol. 3. McGraw-Hill/Irwin, Boston.
14. M. G. Elfeky, W. G. Aref, and A. K. Elmagarmid. 2005. Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng. 17, 7 (2005), 875--887.
15. Eibe Frank, Michael Mayo, and Stefan Kramer. 2015. Alternating model trees. In Proceedings of the 30th Annual ACM Symposium on Applied Computing (SAC'15). ACM, New York, NY. DOI:http://dx.doi.org/10.1145/2695664.2695848
16. D. Gao, J. Zhou, and L. Xin. 2001. SVM-based detection of moving vehicles for automatic traffic monitoring. In Proceedings of the IEEE Intelligent Transportation Systems. 745--749.
17. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. 2007. Workload analysis and demand prediction of enterprise data center applications. In Proceedings of the IEEE 10th International Symposium on Workload Characterization (IISWC'07). 171--180.
18. J. G. D. Gooijer and R. J. Hyndman. 2006. 25 years of time series forecasting. Int. J. Forecast. 22, 3 (2006), 443--473.
19. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. 2009. The WEKA data mining software: An update. SIGKDD Explor. 11, 1 (2009). http://www.cs.waikato.ac.nz/ml/weka/.
20. S. Haykin. 1999. Neural Networks: A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs, NJ.
21. Rob Hyndman. 2015. Time series data library, DataMarket. https://datamarket.com/data/list/?q=provider:tsdl.
22. Thorsten Joachims. 2006. Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 217--226.
23. D.-C. Juan, S. Garg, and D. Marculescu. 2014. Statistical peak temperature prediction and thermal yield improvement for 3D chip multiprocessors. ACM Trans. Des. Autom. Electron. Syst. 19, 4, Article 39 (Aug. 2014), 23 pages.
24. H. W. Kuhn and A. W. Tucker. 1951. Nonlinear programming. In Proceedings of the 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics. University of California Press, Berkeley, CA, 481--492.
25. Z. H. Li and J. W. Han. 2014. Mining periodicity from dynamic and incomplete spatiotemporal data. In Data Mining and Knowledge Discovery for Big Data, Springer, 41--81.
26. H. T. Lin and C. J. Lin. 2003. A study on Sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei. http://www.csie.ntu.edu.tw/cjlin/papers/tanh.pdf.
27. B. H. Meyer, A. S. Hartman, and D. E Thomas. 2014. Cost-effective lifetime and yield optimization for NoC-based MPSoCs. ACM Trans. Des. Autom. Electron. Syst. 19, 2 (2014), 12.
28. K.-R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. 1997. Predicting time series with support vector machines. In Proceedings of the 7th International Conference on Artificial Neural Networks (ICANN'97). Lecture Notes in Computer Science, vol. 1327, Springer, Berlin Heidelberg, 999--1004.
29. J. Moody and J. Utans. 1994. Architecture selection strategies for neural networks: Application to corporate bond rating prediction. In Neural Networks in the Capital Markets, Wiley Finance.
30. R. S. Pindyck and D. L. Rubinfeld. 1981. Econometric Models and Economic Forecasts. Vol. 2. McGraw-Hill, New York, NY.
31. R. Reyna, A. Giralt, and D. Esteve. 2001. Head detection inside vehicles with a modified SVM for safer airbags. In Proceedings of the IEEE Intelligent Transportation Systems Conference. 268--272.
32. R. Rosales, M. Glass, J. Teich, B. Wang, Y. Xu, and R. Hasholzner. 2014. M AESTROHolistic actor-oriented modeling of nonfunctional properties and firmware behavior for MPSoCs. ACM Trans. Des. Autom. Electron. Syst. 19, 3 (2014), 23.
33. R. Schneider, D. Goswami, S. Chakraborty, U. Bordoloi, P. Eles, and Z. B. Peng. 2014. Quantifying notions of extensibility in FlexRay schedule synthesis. ACM Trans. Des. Autom. Electron. Syst. 19, 4, Article 32 (2014).
34. A. J. Smola and B. Schölkopf. 2004. Statistics and computing. Tutor. Support Vector Regress. 14, 3 (2004), 199--222.
35. A. Sorjamaa, J. Hao, N. Reyhani, Y. Ji, and A. Lendasse. 2007. Methodology for long-term prediction of time series. Neurocomput. 70, 1618 (2007), 2861--2869.
36. C.-H. Tu, H.-H. Hsu, J.-H. Chen, C.-H. Chen, and S.-H. Hung. 2014. Performance and power profiling for emulated android systems. ACM Trans. Des. Autom. Electron. Syst. 19, 2 (2014).
37. V. N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer, New York.
38. C.-H. Wu, J.-M. Ho, and D. T. Lee. 2004. Travel-time prediction with support vector regression. IEEE Trans. Intell. Transport. Syst. 5, 4 (2004), 276--281.
39. L. D. Xu. 2011. Enterprise systems: State-of-the-art and future trends. IEEE Trans. Indust. Inform. 7 (2011), 630--640.
40. F. Ye, Z. Zhang, K. Chakrabarty, and X. Gu. 2013. Board-level functional fault diagnosis using artificial neural networks, support-vector machines, and weighted-majority voting. IEEE Trans. Comput.-Aid. Des. Integr. Circuits Syst. 32, 5 (May 2013), 723--736.
41. F. Ye, Z. Zhang, K. Chakrabarty, and X. Gu. 2014. Board-level functional fault diagnosis using multikernel support vector machines and incremental learning. IEEE Trans. Comput.-Aid. Des. Integr. Circuits Syst. 33, 2 (2014), 279--290.
42. J. Zeng, I.-J. Lin, E. Hoarau, and G. Dispoto. 2010. Productivity analysis of print service providers. J. Imag. Sci. Technol. 54, 6 (2010).

Index Terms

• Information systems

  • Information systems applications