W-TSF: Time Series Forecasting with Deep Learning for Cloud Applications

Arnak Poghosyan

Arnak Poghosyan, Ashot Harutyunyan, Naira Grigoryan, Clement Pang, George Oganesyan, Sirak Ghazaryan, Narek Hovhannisyan

VMware, Inc.




One of the main targets of application performance managers is monitoring of cloud environments with high-velocity custom metrics and analytics. The key components of time series data analytics are forecasting and anomaly detection. The classical methods of time series forecasting were recently empowered by neural network-based models which gain increasing popularity due to their flexibility and ability to tackle complex non-linear problems. Meanwhile, some of the disadvantages of that approach mitigate expectations and require specific solution for SaaS applications. The first challenge for network-based models is resource utilization due to GPU trainings. SaaS applications are extremely sensitive to luxury resources due to high costs. The second challenge is inability of the networks to handle non-stationary time series data that behave with trend and/or seasonality. In this paper, we propose W-TSF, a time series forecasting engine that was preliminary designed for Wavefront by VMware, a monitoring tool for cloud environments. W-TSF resolves all mentioned problems. Implementation and testing for real-customer time series data proved its acceptable capabilities for cloud environments in terms of prediction accuracy and re-source consumption.



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