Dynamic Selection of Virtual Machines for Application Servers in Cloud Environments

TL;DR

This paper presents a real-time, machine learning-based approach for dynamically selecting the most suitable VM types in cloud application servers, improving cost efficiency and adaptability to workload changes.

Contribution

It introduces a novel online machine learning method for dynamic VM type selection that adapts to workload and system changes in cloud environments.

Findings

Reduces total cost compared to standard autoscaling methods

Adapts quickly to workload fluctuations

Proven effective on AWS EC2 with CloudStone benchmark

Abstract

Autoscaling is a hallmark of cloud computing as it allows flexible just-in-time allocation and release of computational resources in response to dynamic and often unpredictable workloads. This is especially important for web applications whose workload is time dependent and prone to flash crowds. Most of them follow the 3-tier architectural pattern, and are divided into presentation, application/domain and data layers. In this work we focus on the application layer. Reactive autoscaling policies of the type "Instantiate a new Virtual Machine (VM) when the average server CPU utilisation reaches X%" have been used successfully since the dawn of cloud computing. But which VM type is the most suitable for the specific application at the moment remains an open question. In this work, we propose an approach for dynamic VM type selection. It uses a combination of online machine learning techniques, works in real time and adapts to changes in the users' workload patterns, application changes as well as middleware upgrades and reconfigurations. We have developed a prototype, which we tested with the CloudStone benchmark deployed on AWS EC2. Results show that our method quickly adapts to workload changes and reduces the total cost compared to the industry standard approach.