Predictive and prescriptive analytics for performance optimization: Framework and a case study on a large-scale enterprise system

John, I and Karumanchi, R and Bhatnagar, S (2019) Predictive and prescriptive analytics for performance optimization: Framework and a case study on a large-scale enterprise system. In: Proceedings-18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 16-19, December 2019, United States, pp. 876-881.

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Abstract

In any industrial or software system, predicting future values of measurable parameters well in advance is of utmost importance for avoiding disruptions. The historical data on system parameters measured at regular time intervals can be leveraged to address this long horizon prediction problem. However, complex interdependencies between the parameters and the need for avoiding false recommendations pose challenges in this prediction task. An equally challenging and useful exercise is to identify the 'important' parameters and optimize them in order to attain good system performance. This paper describes a generic framework, along with specific methods, for this data analytics problem and presents a case study on a large-scale enterprise system. The proposed method combines techniques from machine learning, causal analysis, time-series analysis and stochastic optimization to achieve accurate prediction (estimating future values of parameters) and reliable prescription (controlling independent parameters to optimize system performance). The approach is validated with data from a large-scale enterprise service bus consisting of about 30 parameters measured at 5 minute intervals over a period of 6 months.

Item Type:	Conference Paper
Publication:	Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 ; Conference Date: 16 December 2019 Through 19 December 2019; Conference Code:157875
Keywords:	Data Analytics; Forecasting; Machine learning; Optimization; Predictive analytics; Support vector regression; Time series, Causal analysis; Enterprise service bus; Independent parameters; Measurable parameters; Optimization method; Performance optimizations; Prediction methods; Stochastic optimizations, Time series analysis
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	17 Aug 2020 06:58
Last Modified:	17 Aug 2020 06:58
URI:	http://eprints.iisc.ac.in/id/eprint/64907

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