Autoscaling Solutions for Cloud Applications Under Dynamic Workloads

Giovanni Quattrocchi

doi:10.1109/TSC.2024.3354062

Autoscaling Solutions for Cloud Applications Under Dynamic Workloads

2024-05-01

8

OA

AI

DOI：10.1109/TSC.2024.3354062

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Autoscaling systems provide means to automatically change the resources allocated to a software system according to the incoming workload and its actual needs. Public cloud providers offer a variety of autoscaling solutions, ranging from those based on user-written rules to more sophisticated ones. Originally, these solutions were conceived to manage clusters of virtual machines, while more recently, they have also been employed in the operation of containers. This article analyses the autoscaling solutions provided by three major cloud providers, namely Amazon Web Services, Google Cloud Platform, and Microsoft Azure, and compares them against two solutions we develop based on control theory (ScaleX) and queuing theory (QN-CTRL). We evaluate the different approaches using both an in-house simulation engine and cloud deployments by feeding them with various synthetic and real-world workloads. Our extensive evaluation collects both simulation results and real measurements by which we can assess that both ScaleX and QN-CTRL outperform industrial techniques in most cases when considering the trade-offs between the service-level-agreement (SLA) violations and the optimal usage of resources.

Keyword:

Containers

Cloud computing

Measurement

Scalability

Web services

Dynamic scheduling

Monitoring

Autoscaling

elastic computing

cloud computing

containerization

containers

control theory

optimal control

Autoscaling Solutions for Cloud Applications Under Dynamic Workloads

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