Predictive Auto-scaling with OpenStack Monasca

TL;DR

This paper presents a predictive auto-scaling architecture for cloud services using machine learning-based time-series forecasting, integrated into OpenStack with Monasca, to anticipate load peaks and improve scaling responsiveness.

Contribution

It introduces a novel predictive auto-scaling framework that leverages machine learning models within OpenStack, extending Monasca for proactive resource management.

Findings

Predictive policies outperform reactive auto-scaling in anticipating load peaks.

Recurrent neural networks and multi-layer perceptrons provide accurate future metric predictions.

The framework is customizable and integrates seamlessly with existing OpenStack components.

Abstract

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed.