Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Version 1 : Received: 16 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (12:00:39 CET)

How to cite: Poghosyan, A.; Harutyunyan, A.; Grigoryan, N.; Pang, C.; Oganesyan, G.; Ghazaryan, S.; Hovhannisyan, N. An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning. Preprints 2021, 2021010326 (doi: 10.20944/preprints202101.0326.v1). Poghosyan, A.; Harutyunyan, A.; Grigoryan, N.; Pang, C.; Oganesyan, G.; Ghazaryan, S.; Hovhannisyan, N. An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning. Preprints 2021, 2021010326 (doi: 10.20944/preprints202101.0326.v1).

Abstract

One of the key components of application performance monitoring (APM) software is 2 AI/ML empowered data analytics for predictions, anomaly detection, event correlations and root 3 cause analysis. Time series metrics, logs and traces are three pillars of observability and the valuable 4 source of information for IT operations. Accurate, scalable and robust time series forecasting and 5 anomaly detection are desirable capabilities of the analytics. Approaches based on neural networks 6 (NN) and deep learning gain increasing popularity due to their flexibility and ability to tackle complex 7 non-linear problems. However, some of the disadvantages of NN-based models for distributed cloud 8 applications mitigate expectations and require specific approaches. We demonstrate how NN-models 9 pretrained on a global time series database can be applied to customer specific data using transfer 10 learning. In general, NN-models adequately operate only on stationary time series. Application 11 to non-stationary time series requires multilayer data processing including hypothesis testing for 12 data categorization, category specific transformations into stationary data, forecasting and backward 13 transformations. We present the mathematical background of this approach and discuss experimental 14 results from the productized implementation in Wavefront by VMware (an APM software) while 15 monitoring real customer cloud environments.

Subject Areas

time series analysis; anomaly detection; neural networks; hypothesis testing; trend 17 analysis; periodicity analysis; cloud applications; pretrained models; transfer learning

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