A Hybrid Mechanism of Horizontal Auto-scaling Based on Thresholds and Time Series
Paulo Pereira, Jean Carlos Teixeira de Araujo, Paulo Maciel · 2019
Demand for performance, availability, and reliability in computational systems has increased lately. Improving these aspects is an important research challenge due to the applications and users diversity. In this paper, we propose a hybrid auto-scaling approach that uses reactive and proactive solutions. We focus on the operation of a Web server, where we applied the characteristic of CPU-bound in the application. The approach proposed is a CPU usage monitor and auto-scaling management that is used to improve the system's throughput and reduce the CPU idle time. Our proposal is implemented using triggering thresholds and five forecasting models: Drift, Simple Exponential Smoothing, Holt, Holt-Winters and ARIMA. The main goal of this research is to achieve a better QoS related to the cloud computing environment. One of the obtained results shows that our method represents a throughput improvement of 12.11% by using our proposal, instead of only using a threshold-based technique.