Theta-Scan: Leveraging behavior-driven forecasting for vertical auto-scaling in container cloud

Abstract

Detection of behavior patterns on resource usage in containerized Cloud applications is necessary for proper resource provisioning. Applications can use CPU/Memory with repetitive patterns, following a trend over time independently. By identifying such patterns, resource forecasting models can be fit better, reducing over/under-provisioning via fewer resizing operations. Here we present ThetaScan, a time-series analysis method for vertical auto-scaling of containers in the Cloud, based on the detection of stationarity/trending and periodicity on resource consumption. Our method leverages the Theta Forecaster algorithm with deseasonalization that, in our provisioning scenario, only requires the estimated periodicity for resource consumption as principal hyper-parameter. Commonly used behavior detection methods require manual hyper-parameter tuning, making them infeasible for automation. Besides, it can be used at multi-scales (minute/hour/day), detecting hourly and daily patterns to improve resource usage prediction. Experiments show that we can detect behaviors in resource consumption that common methods miss, without requiring extensive manual tuning. We can reduce the resizing triggers compared to fixed-size scheduling around ~ 10% – 15%, reduce over-provisioning of CPU and Memory through periodic-based provisioning. Also a ~ 60% on multiscale resource forecasting for traces showing periodicity at different levels in respect to single-scale.

This work has been partially supported by the Spanish Government (contract PID2019-107255GB) and by Generalitat de Catalunya (contract 2014-SGR-1051).

Peer Reviewed

Postprint (author's final draft)